• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

    2022-08-23 02:20:38DurrNayabAliMustafaQamarRehanUllahKhanWaleedAlbattahKhalilKhanShabanaHabibandMuhammadIslam
    Computers Materials&Continua 2022年6期

    Durr-e-Nayab,Ali Mustafa Qamar,Rehan Ullah Khan,Waleed Albattah,Khalil Khan,Shabana Habib and Muhammad Islam

    1Department of Computer Systems Engineering,University of Engineering and Technology,Peshawar,Pakistan

    2Department of Computer Science,College of Computer,Qassim University,Buraydah,Saudi Arabia

    3Department of Information Technology,College of Computer,Qassim University,Buraydah,Saudi Arabia

    4Department of Information Technology and Computer Science,Pak-Austria Fachhochschule,Institute of Applied Sciences and Technology,Haripur,Pakistan

    5Department of Electrical Engineering,College of Engineering and Information Technology,Onaizah Colleges,Al-Qassim,Saudi Arabia

    Abstract: The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research, and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed, and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object (person) moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity to maintain a smooth crowd flow in Kaaba during the pandemic.

    Keywords: Computer vision; COVID sparse crowd; crowd analysis; flow analysis;sparse crowd management;tawaaf video analysis;video processing

    1 Introduction

    Public places like theme parks,airports,and even mosques used to be crowded in general.With the emergence of the COVID-19 pandemic and the enforcement of social distancing policies,the crowds have now reduced and are primarily organized[1].The first case of COVID-19 was reported in Wuhan,China,in December 2019[2].The World Health Organization declared COVID-19 as a pandemic on March 11,2020.The number of coronavirus cases stands at more than 211 Million as of August 23,2021.Moreover,more than 4.4 million have died from COVID-19[3].

    The automatic monitoring of crowds has become even more critical from a security and health point of view.Among the most effective measures to limit the spread of coronavirus are face masks and the implementation of social distancing.Crowd density,the average number of people in a scene,could be exploited to determine the crowd’s direction [4].Crowd events such as local dispersion, running,crowd splitting,evacuation,and crowd formation must be identified quickly[5].

    Makkah and Medina are the two holiest places for Muslims.Makkah is home to the Kaaba,the most sacred site in Islam.Muslims around the world pray in the direction of the Kaaba.Circling the Kaaba seven times in a counter-clockwise direction, also referred to as Tawaaf, is obligatory to complete Umrah and Hajj pilgrimages.In this research, we analyze the flow of the crowd while performing Tawaaf during the COVID-19 pandemic.The dynamics of the crowd have dramatically changed during the COVID-19 pandemic.The crowds are now generally sparse in nature because of social distancing.The same trend is observed during Tawaaf.More than 2.5 Million Muslims gather in Makkah yearly to perform Hajj.However,only 1000 worshipers were allowed to proceed for Hajj in 2020 and around 60000 in 2021 due to the ongoing COVID-19 pandemic.This research presents a novel data set related to Tawaaf,gathered during the COVID-19 pandemic.We developed a system to detect the movement of the crowd along with finding different kinds of anomalies.

    The rest of the paper is organized as follows: the motivation is provided in Section 2, and the contributions along with the research gap are given in Section 3.Similarly, the state-of-the-art is discussed in Section 4, followed by the proposed methodology in Section 5.Section 6 presents the experimental setup,while the results and discussion are provided in Section 7.Some applications of anomaly detection are discussed in Section 8.The article is concluded in Section 9,along with giving some future directions.

    2 Motivation

    Before COVID-19, the congestion and crowding in the two holy mosques were normal and acceptable.However, after the emergence of COVID-19, the situation became precarious due to the possibility of infection spread between pilgrims and visitors.The flow of people within the holy mosques must be well organized and monitored to ensure the physical distancing during the crowd motion.

    The motivation of the crowd motion in this article is to propose a vertical and horizontal-based framework for automating the task of monitoring social distancing during the Tawaaf of Kaaba.

    The proposed framework utilizes a combination of Shi-Tomasi and Lucas-Kanade models to detect moving pedestrians.An algorithm has been developed to segregate humans from the background and track the identified people with the help of horizontal and vertical flows to identify the non-majority flow.Motivated by this,in this present work,the authors are attempting to check and compare the performance of object detection by using the histogram to monitor social distancing.

    3 Contributions and the Research Gap

    In this paper, the authors analyzed the moment behavior related to factors affecting the risk of COVID-19 moment in the crowd during Tawaaf in Kaaba.Since one of the most important ways to avoid exposure is to reduce contact with other people,the authors measured the distance between the people during the Tawaaf.For this purpose, the research obtained preferences between a crowdedbut-low-wait-time and a less crowded but higher wait time alternative.The research gap is presented in the context of exposure duration (operationalized as the moment time of the alternatives) and infection rate to examine the effects of these risk-contributing factors on choice behavior.The data were collected from the Kaaba during Tawaaf at the end of the first infection wave, just as the first restrictions were being lifted and new regulations were set up for Umrah in Kaaba travel.The authors believe that behavioral insights from this study will not only contribute to better demand forecasting but will also be valuable in informing the pandemic decisions for Umrah.

    4 Literature Review

    Hajj and Umrah crowd management is a challenging task even in the normal situations before the COVID-19 pandemic, which is due to different reasons.People gather in very limited areas coming from all over the world with different languages and cultures[6].Most of them have not been to the two holy cities of Makkah and Madina before,and they do not have experience with the environment,which is often reflected in people’s behaviors during Hajj and Umrah.Due to this, pilgrims usually move as groups of people with a guide to provide them with instructions and rules and answer their queries about performing the worship activities.This makes the movements of such groups represent another challenge to crowd management.

    Anees et al.[4] developed an approach to determine the direction of the global movement of the crowd.The dense areas were identified using key-point descriptors,which ultimately contributed to finding the flow direction.Many researchers have performed extensive surveys related to crowd analysis using surveillance videos.One such survey was performed by Zhang et al.[7], where the researchers focused only on physics-based methods.Just like physics,a crowd video could be analyzed from three different angles:microscopic,mesoscopic,and macroscopic.In microscopic methods,the behavior of each individual is analyzed.Such methods could be applied to a small crowd but become too tedious to handle for a large-scale crowd.On the other hand, macroscopic approaches treat the crowd as a whole.Such techniques determine the crowd’s behavior by the collective performance and are most suited for large-scale crowds with the same movement pattern.Mesoscopic methods could be considered hybrid and consider the pros and cons of microscopic and macroscopic levels[8].

    Fradi[9]developed a hybrid method while considering the long-term trajectories to consider local and global attributes.In this way,he was able to determine the motion in the given video.The local crowd density was used along with crowd speed and orientation.He discussed that running events are generally characterized by calculating the speed.Nevertheless,it is also essential to determine the number or density of people implicated in these events.The evacuation event was identified using attributes like speed, direction, and crowd density.This event can be detected using four principal directions,which have to be distant from one another.A reduction in the density,an increase in speed,and the motion area indicate that evacuation is being done.However,a crowd formation event occurs when many persons from different directions merge at the same location.Here, an increase in the density and a decrease in the motion area are observed.100% precision and a recall value of 92.5%were observed for crowd change detection.Similarly, the crowd event recognition method achieved accuracy values of 100%for splitting,99.8%for evacuation,and 99.5%for formation.

    Nam [10] developed an approach to detect abnormal events from structured and unstructured motion and flows of crowds.He considered features like the speed and the direction of moving objects in videos.The experiments are conducted on highways,crosswalks,and escalators.The flux analysis yielded the types of moving patterns.The proposed algorithm was able to detect wrong-way driving on a T-junction.Anomalies were detected in crowds by Irfan et al.[11].The researchers classified the movement patterns into normal and abnormal activities using the Random Forest algorithm.The videos were made using mobile phones,and the system was presented as an alternative to video sensors.In another research, Li [12] developed a crowd density estimation algorithm specific to touristic places.She discussed that business managers neither want too high crowd density nor need too-small density.A too big value can lead to a stampede, and a too-small value might not be commercially feasible.The crowd density monitoring could be performed in real-time by analyzing the behavior of the crowd movement.The author combined the agglomeration and the crowd density to get a novel algorithm.Whereas aggregation refers to the degree to which a person participates in a group movement, agglomeration represents the crowd’s density and is directly proportional to the density[13].Baqui et al.[14]developed a model to perform real-time monitoring of the Hajj.The researchers used the footage obtained from the closed-circuit television(CCTV)cameras in the Tawaaf area.Six hundred image segments were manually annotated using dot annotation.In this technique, a dot is placed on all the heads present in a segment.The input images were divided into 100 parts.It took 32.79 s to process just two frames of the dataset.

    L?hner et al.[15] developed two models to describe the motion in the Mataaf region.The first model allocates a preferred distance from the Kaaba to each pilgrim.In this way, the model could be used to enforce social distancing in the context of COVID-19.The second model assumes that the pilgrim wants to get closest to the Kaaba until a tolerable density is achieved.The models were implemented in PEDFLOW[16],a pedestrian flow and crowd simulation software.Lohner et al.[17]ran an experimental campaign to measure the flow of the pilgrims during the Hajj season of 2014 and 2015.An increase in velocity was observed in the high-density regions.This increased velocity pointed to an increase in the flux for higher density regions(more than eight persons/m2).The flux increased to more than 3.2 persons/meter/second,more than any flux reported to date.

    In a recent study,Kolivand et al.[18]simulated crowd movements at the Tawaf area using a highdensity model.The model was more realistic by considering some attributes of people such as gender,movement speed, and stopping in the crowd.One of the study’s interesting findings is that as many people in the group as many stops will occur in the crowd.However,the study was short in identifying the potential bottleneck locations in the Tawaf area where frequent stops of pilgrims happen.Bouhlel et al.[19]developed macroscopic and microscopic techniques using convolutional neural networks to monitor social distancing using UAVs.The macroscopic method focuses on crowd density and crowd flow and categorizes aerial frames into dense,medium,sparse,and none.Similarly,in sparse crowds,the microscopic method helps to find the distance between humans.

    5 Proposed Methodology

    In this section,we discuss the details of the proposed methodology.

    5.1 Sparse Optical Flow Analysis

    The optical flow analysis mechanism proposed in this work is used for predicting and analyzing the direction, position, and velocity of the crowd in the video.The optical flow in a video is termed the motion of objects between consecutive frames.We assume that the pixel intensities are constant between frames.The motion in thex(horizontal) andy(vertical) directions is expressed mathematically in Eq.(1).

    whereIrepresent the intensity,xandyare the horizontal and vertical space coordinates,tis the time slot,anddx,dy,anddtare the changes in the mentioned coordinates.The Taylors series approximation and division ondtare used to get the optical flow equation as shown in Eq.(2).

    whereu=dx/dt,v=dy/dt,and?I/?x,?I/?y,and?I/?tare the image gradients along the horizontal and vertical axis and time parameters.To solve the optical flow equation as shown in Eq.(2),we need to solve the equation for?I/?xuand?I/?yvto attain movement over time.This requires solving for two unknowns?I/?xuand?I/?yv,which is not a straightforward process.Hence,we apply the Lucas-Kanade technique[20]and the one developed by Manenti et al.[21]to find these unknowns.

    5.2 Sparse Features Analysis:Shi-Tomasi Corner Detector Technique

    The features that are used for sparse crowd videos are edges and corners.The Shi-Tomasi[22]and Olson[23]techniques compute the flow over small patches taking the local method and considering the flow constant for all pixels.Shi-Tomasi corner detector tracks pixels locally to track the motion of the feature set of all pixels.The Shi-Tomasi technique first determines the windows of small patches with large gradients, i.e., image intensity variations when translated inxandydirections.We later compute the R score to identify the window as flat,edge,or corner in the Shi-Tomasi scoring function,mathematically shown in Eq.(3).

    whereλ1andλ2are Shi-Tomasi window space,which means that if R is greater than a threshold,it is classified as a corner.For Shi-Tomasi,only whenλ1andλ2are above a minimum threshold(λmin),is the window classified as a corner,while in caseλ1 >λ2orλ1 <λ2then the window is considered to be an edge and uniform or a flat region otherwise.Fig.1 gives an illustration of Shi-Tomasi corner detection inλ1–λ2space.The key considerations by the Shi-Tomasi technique for each pixel are that each pixel has the following properties:

    1) Color Constancy

    2) Brightness Constancy

    3) Small motion with respect to nearby pixels

    Figure 1:An illustration of Shi-Tomasi corner detection in λ1–λ2 space

    5.3 Tracking Specific Object:Lucas-Kanade Technique

    For tracking a specific object in a frame, a previous frame with extracted features is used.The features of the previous frame are compared with the current frame for tracking specific objects.This comparison provides information about the motion of interesting features by comparing the consecutive frames.Iterative image registration is carried out with the Lucas-Kanade method that estimates motion in Tawaaf videos.The Lucas-Kanade technique,also known as the Lucas-Kanade translational warp model,uses the image frame-by-frame for three kinds of analysis[20].These three kinds of analysis include spatial analysis as depicted in Eqs.(4)and(5),optical flow analysis as shown in Eqs.(6)and(7),and temporal analysis as shown in Eq.(8).

    The Lucas-Kanade translational warp model takes two consecutive frames separated by a short time interval (dt) that is kept short on purpose for attaining good performance on slowly moving objects.A small window is taken within each frame to be used around the features detected by the Shi-Tomasi corner detecting filter.The motion is detected from each set of consecutive frames if single or multiple points within the window are moving.It is assumed that the whole frame is moving if a movement in the window is detected.This way,the movement is detected at the lowest resolution and systematically moved to the whole image frame,i.e.,higher resolution.Fig.2 illustrates the windowing process (N×Nneighborhood) of Lucas-Kanade around Shi-Tomasi features.The wholeN×Nwindow is assumed to have the same motion.

    Figure 2: An illustration of the windowing process (N×N neighborhood) of Lucas-Kanade around Shi-Tomasi features

    The Lucas-KanadeN×Npixels’intensities can be represented in Eqs.(9),(10),and(11).

    wherep1, p2, ..., pnare the pixels inside each window, andIx (pi),Iy (pi), andIt (pi)represent the partial derivatives of the imageIwith respect to the position(x,y)and timet.For instance,if a window of size 3×3 is used,the value forn=9andN=3.Vx=u=dx/dt,as discussed earlier,is the horizontal movement ofxover time andVy=v=dy/dtis the vertical movement ofyoverdt.In short,we identify some interesting features to track and iteratively compute the optical flow vectors of these points.The Lucas-Kanade method goes stepwise from a small-level view to a high-level view,where small motions are neglected and large motions are reduced to small motions.This is the shortcoming of the method as it works for small movements only and fails to optimally detect the large movements as the short movements do not represent the large movements.

    5.4 Detecting Abnormal Flow:Maximum Histogram Technique

    Once the flow coordinates are attained through Shi-Tomasi and Lucas-Kanade techniques,separate histograms are generated for each horizontal and vertical motion.These histograms are taken to analyze the maximum flow in the video while using this information to detect any motion not in the same direction as that of the maximum flow.As in Tawaaf,the maximum crowd moves in a similar direction; this feature can detect an anomaly or abnormal movement in the crowd.We take manual thresholds for each video as each video has a different crowd and behavior,and as future enhancement of this work,we propose to deploy ML algorithms to automate the value of these thresholds for all kinds of videos.Our proposed algorithm detects any unusual flow against the standard anticlockwise flow and spots any lateral movements,such as the pilgrims weaving to the left or right.The notations used in this paper are summarized in Tab.1.

    Table 1: Notations used in the paper

    5.5 Proposed Algorithm and Flowchart

    In order to analyze the Tawaaf video for crowd analysis and anomaly detection, we take the video frame-by-frame and track the movement of the crowd in the vertical and horizontal directions.These motions are traced using the iterative Shi-Tomasi algorithm’s corner detection mechanism for finding the strongest corners in the frame.The details are presented in Fig.3.After finding the corners in the first frame, an iterative algorithm is applied to each consecutive set of frames to compute the flow inx(vertical) andy(horizontal) directions.The Lucas-Kanade technique is used for flow computation.The coordinates forxandydirection motions are tallied and stored into histograms to analyze the maximum motion quickly.These histograms are deployed to detect any motion in a nonmajority direction and mark it as abnormal flow.The thresholds are selected manually by thoroughly analyzing the histograms while focusing on the regions of condensed displacements in the vertical and horizontal directions.The regions in histograms where the displacements become low are marked as limits for the thresholds.After marking the thresholds, the response is observed and verified in the video.This process is repeated unless the optimal thresholds are selected and improved results are obtained.Initially,the thresholds are set manually for each video and utilized to automate the normalvs.abnormal flow.

    Figure 3:Flowchart of the proposed approach

    The stepwise implementation of the proposed algorithm in the form of Pseudo-Code is provided in Algorithm 1.

    Algorithm 1:Sparse Crowd Flow Analysis Algorithm Read the video frames First=Capture the first frame while(The last frame is not reached)(Continued)

    grey1=Convert the image frame to greyscale Get the parameters for Lucas-Kanade optical flow Get the features parameters for Shi-Tomasi corner detection Apply cv.goodFeaturesToTrack()function to get the strongest corners Create image first frame mask with zeros Second=Capture the next frame Grey2=Convert the image frame to greyscale Apply cv.calcOpticalFlowPyrLK()function to get the optical flow Select good feature points for previous and next positions Draw the optical flow tracks using contiguous flattened array as(x,y)coordinates for new(c,d)and old(a,b)points Append the change in x and y coordinates in an array bucket element.append(d)using 100 bins Plot the histogram of changes:plt.hist(element,bins=100)Show the histogram:plt.show()Evaluate the maximum change using pdf from 100 bins Filter the maximum changes using Threshold_1 for x-coordinate and Threshold_2 for y-coordinate if((d-b)>Threshold_1)or(c-a)>Threshold_2)Mark the points in frame as anomaly:frame=cv.circle(frame,(a,b),1,(0,0,255),10)Overlay the optical flow tracks on the original frame output=cv.add(mask,frame)Update previous frame:prev_gray=gray.copy()Update previous good feature points prev=good_new.reshape(-1,1,2)Open new window and display the output frame cv.imshow(“Sparse Anomaly”,output)else x=a+c #Evaluate total change in x-coordinate y=b+d #Evaluate total change in y-coordinate w=c–a #Evaluate change in x-coordinate z=d–b #Evaluate change in y-coordinate end if Read frames at 10 ms intervals till the last frame.if(cv.waitKey(10)&0xFF==ord(‘q’):break #if user quits the video end if end while cap.release()cv.destroyAllWindows()

    Figure 4: Flow analysis of sparse crowd in Tawaaf showing the majority (marked green) flow vs.abnormal flow(marked red)in Video 1

    6 Experimental Setup

    Python libraries are employed to implement our algorithms, such as OpenCV-python libraries,to read the video file and set up various parameters to pre-process the video files.The video files are initially converted to the grayscale frame-by-frame so that the algorithms and methods can be applied to them.The Shi-Tomasi technique selects the pixels for tracking and finds the strongest corners in a frame usingcv.goodFeaturesToTrack()implementation in OpenCV.For the detection of motion of an object,the Lucas-Kanade algorithm is applied on consecutive frames over a small-time durationdt.The OpenCV implementation of Lucas-KanadecalcOpticalFlowPyrLK()is employed for flow analysis.calcOpticalFlowPyrLK()returns the next frame,status of a motion,and error message determining if the frame is not suitable for detecting the motion.The function takes as input the previous frame,its grayscale value,previous frame good features,and other parameters for the Lucas-Kanade technique.Both the corners and the motions are traced in separate masks,and frame overlays throughcv.line()andcv.mask()functions are added on each video frame after computation of motion usingcv.add() function.The evaluation was performed on a set of Tawaaf videos obtained from YouTube.The videos are collected during Hajj 2020 and regular Umrah being performed after the emergence of COVID-19.The videos contain only a sparse crowd owing to the social distancing rules implemented since COVID-19.Each video is of 20 s duration and is in MP4 format.The frames are taken at each 10 ms, i.e., 100 frames per second are taken.We have analyzed seven different video samples of Tawaaf and provided the results of four videos in Section 7.

    7 Results and Discussion

    We have evaluated various short videos from Tawaaf of the Kaaba during the COVID-19 pandemic when the crowd is sparse and maintains social distancing.The annotation is added in the video through coding by marking the majority flow of each object(crowd)in green trails and showing the track of the flow while processing the video.The video is analyzed through histograms to evaluate the maximum flow,and thresholds are applied on the flow counts to separate the abnormal movement marked through coding annotation in a red circle.

    Figure 5:Histogram of horizontal flow in Video 1

    Fig.4 shows the screenshots from the first video analysis of Tawaaf that illustrate the crowd tracks in majority directions marked in green and minority movements are marked red and depict abnormal or anomaly movements.These annotations are done through coding,whereas the squares to highlight the abnormal movements are inserted manually,which can be automated later on.It can be observed from the tracks in Fig.4 that the objects(people)marked inside the squares are either still or moving in directions that are not the same as the majority.In Tawaaf,the crowd flow follows similar tracks with respect to each other and does not follow the same directions in general because the flow around Kaaba is circular,i.e.,forward,backward,upward,and downward.Hence,specifying a particular direction as an anomaly does not imply in the case of Kaaba crowd analysis,but specifying the majority flow and tracking the flow against the majority does imply.Some of the movements that are not characterized correctly are highlighted with red dotted squares,which we aim to improve in the future as an extension of this work using machine learning techniques.

    Figs.5 and 6 show the histograms of horizontal and vertical flow,respectively.The histograms are taken while the video frames are being read.It can be observed from the histograms that the aggregate of the individual movement increase as more frames are read,but there are regions where the aggregates are very minute and not changing much.We target these minute movements and applied thresholds on such small movements.For instance,in Video 1,the horizontal flow is concentrated between 150 and 550,and the vertical flow is concentrated around 330.We use the vertical threshold in Video 1 as the vertical histograms are more intricate than the horizontal ones.

    Figure 6:Histogram of vertical flow in Video 1

    Fig.7 shows the results from the second video analysis of Tawaaf.It illustrates the crowd tracks in majority directions marked in green,and minority movements are marked red to show abnormal or anomaly movements.It is observed from the tracks in Fig.7 that the objects(people)marked inside the red squares are either still or moving in the directions that are not the same as the majority,while the majority follows the green tracks programmed to show the normal flow.In Video 2, we used a threshold<180 on horizontal flow as most of the movements are concentrated above<180, and in vertical flow, we used the thresholds<230.The horizontal and vertical histograms for Video 2 are shown in Figs.8 and 9.

    Figure 7: Flow analysis of sparse crowd in Tawaaf showing the majority (marked green) flow vs.abnormal flow(marked red)in Video 2

    The details for Video 3 are provided in Fig.10,and the histograms are presented in Figs.11 and 12.The threshold values are engineered manually by observing the histograms that we aim to automate as an extension of this work.

    Another video(Video 4)is analyzed for the sparse crowd.The results are provided in Fig.13 and the histograms in Figs.14 and 15.It is observed in all Tawaaf videos that sharp changes are present either in horizontal or vertical directions or in some cases in both directions.We exploited these sharp changes and devised the thresholds for cutting off the anomalies as any flow is detected in unusual directions.

    Figure 8:Histogram of horizontal flow in Video 2

    8 Applications of Anomaly Detection

    In video analysis, anomaly detection equates to outlier detection in sequences.It is a rare event detection in video sequences based on specific variables,and to flag it as the anomalous state;certain conditions must be satisfied.The goal is to signal an activity that deviates from normal behavior and identifies the anomalous action time window.Thus, anomaly detection is coarse level scene analysis that filters the abnormal pattern from the normal ones.The detection of such rare events and conditions has several applications.It can be helpful in road safety and traffic accidents.In such situations, autonomous anomaly detection can save lives and help avoid congestion in roads having such incidents.Another application is related to crime.This kind of autonomous detection of anomalous events can guide the police and law enforcement agencies towards criminal acts.Anomalous detection of events in such cases can help stop certain crimes and provide justice if the crime has been committed.

    Furthermore,anomaly detection can help on several occasions in a mass gathering.Especially for Hajj and Umrah, where autonomous detection of anomalous behavior can help control the crowd.Also, this can help in avoiding stampedes, which can save thousands of lives.Moreover, in Hajj and mass gatherings, abnormal behavior detection can help medical services to focus on particular conditions and special people during the active Hajj activities.This detection can help save thousands of lives and provide quality services to the pilgrims.

    Figure 9:Histogram of vertical flow in Video 2

    Figure 10: Flow analysis of sparse crowd in Tawaaf showing the majority (marked green) flow vs.abnormal flow(marked red)in Video 3

    Figure 11:Histogram of horizontal flow in Video 3

    Similarly,anomalous behavior detection can also help in different occasions,for example,sports and political gatherings.In these gatherings, anomalous behavior detection can help in pinpointing criminal or unwanted acts.Also, abnormal behavior detection can help in improving the quality of services on such occasions.

    Figure 12:Histogram of vertical flow in Video 3

    Figure 13: Flow analysis of sparse crowd in Tawaaf showing the majority (green tracks) flow vs.abnormal flow(marked red)in Video 4

    Figure 14:Histogram of horizontal flow in Video 4

    Figure 15:Histogram of vertical flow in Video 4

    9 Conclusion and Future Work

    With the advent of the COVID-19 pandemic,the scenarios for the crowd have been significantly affected.On one side,many dense crowd situations have been converted into sparse crowd situations.Conversely,the disease has put forth high demand for managing the crowd remotely,i.e.,without any physical distance.Since the nature of crowds has changed worldwide,the manner in which we address the crowd has also been affected.In the Muslim world,managing Kaaba rituals has been a crucial task since the crowd gathers from around the world and requires to be analyzed differently in the days of the pandemic.In this research,we have considered the case study of Muslim rituals in Kaaba during the COVID-19 pandemic and analyzed the sparse crowd flow.We have analyzed the Umrah videos and monitored the sparse flow of the crowd.The tracks of objects/people are monitored and grouped as normal and abnormal flow.This grouping is done by observing the histograms of the flow in vertical and horizontal directions and applying thresholds on the maximum flow.The majority movement is considered to be normal, while other movements are classified as abnormal or anomaly.We have worked on these videos to track the maximum crowd flow and detect any object (person) moving opposite the significant flow.This detection finds any movement that maintains smooth flow in Kaaba and detects and controls any abnormal activity through video surveillance.The work presented in this paper is initial,and as a future enhancement of this work,we aim to develop an adaptive method for selecting thresholds for anomaly detection and applying machine learning techniques to generalize the algorithm.We also intend to diversify the proposed algorithm’s application by applying it to other crowd videos and extending the work from sparse to dense crowd analysis using deep learning techniques.More specifically,we plan to apply our approach to dense Tawaaf and Sa’i videos collected before the pandemic of COVID-19.

    Funding Statement:The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the Project Number QURDO001.Project title: Intelligent Real-Time Crowd Monitoring System Using Unmanned Aerial Vehicle(UAV)Video and Global Positioning Systems(GPS)Data.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    九草在线视频观看| 肉色欧美久久久久久久蜜桃| 国产在线免费精品| 国产高清不卡午夜福利| 女性生殖器流出的白浆| 亚洲色图av天堂| 岛国毛片在线播放| 99精国产麻豆久久婷婷| 亚洲av二区三区四区| 久热这里只有精品99| 日韩在线高清观看一区二区三区| 男女啪啪激烈高潮av片| 色哟哟·www| 插阴视频在线观看视频| 亚洲人成网站在线播| 91狼人影院| 男的添女的下面高潮视频| 99热网站在线观看| 午夜老司机福利剧场| 中文字幕制服av| 亚洲精品一二三| 国产深夜福利视频在线观看| 人妻一区二区av| 国产永久视频网站| 一边亲一边摸免费视频| 国产精品久久久久久精品电影小说 | 大又大粗又爽又黄少妇毛片口| 亚洲最大成人中文| 亚洲欧美精品专区久久| 久久ye,这里只有精品| 性色avwww在线观看| 精品99又大又爽又粗少妇毛片| 久久亚洲国产成人精品v| 22中文网久久字幕| 97在线视频观看| 国产精品嫩草影院av在线观看| 精品亚洲乱码少妇综合久久| 国产黄片美女视频| 日韩不卡一区二区三区视频在线| 男男h啪啪无遮挡| 国产精品伦人一区二区| 日本黄色日本黄色录像| 亚洲熟女精品中文字幕| 国产精品精品国产色婷婷| 欧美精品亚洲一区二区| 小蜜桃在线观看免费完整版高清| 国产黄色免费在线视频| 久久人人爽av亚洲精品天堂 | 国产 一区精品| 亚洲精品国产成人久久av| 在线 av 中文字幕| 午夜免费观看性视频| 亚洲电影在线观看av| 九九久久精品国产亚洲av麻豆| 久久精品久久精品一区二区三区| 国产精品一二三区在线看| 久久这里有精品视频免费| 午夜福利在线观看免费完整高清在| 色婷婷av一区二区三区视频| 91精品伊人久久大香线蕉| 美女视频免费永久观看网站| 国产精品一区二区三区四区免费观看| 菩萨蛮人人尽说江南好唐韦庄| 国产高清有码在线观看视频| 中文字幕亚洲精品专区| 欧美日韩综合久久久久久| 成人国产麻豆网| 久久青草综合色| 超碰97精品在线观看| 欧美老熟妇乱子伦牲交| 日本av免费视频播放| 少妇裸体淫交视频免费看高清| 麻豆精品久久久久久蜜桃| 成人午夜精彩视频在线观看| 在线观看免费日韩欧美大片 | av免费在线看不卡| 一区二区三区乱码不卡18| 久久久久久久久久久丰满| h日本视频在线播放| 免费播放大片免费观看视频在线观看| 精品久久久久久久末码| 夜夜爽夜夜爽视频| 日韩av不卡免费在线播放| 一级a做视频免费观看| 亚洲精品日本国产第一区| 在线观看一区二区三区激情| 日韩av不卡免费在线播放| 久久久精品94久久精品| 亚洲av综合色区一区| 精品国产乱码久久久久久小说| 国产高清有码在线观看视频| 中文字幕人妻熟人妻熟丝袜美| 波野结衣二区三区在线| 国产精品国产三级国产av玫瑰| 亚洲精品国产av成人精品| 久久久久久久久久久丰满| 久久国产精品大桥未久av | 六月丁香七月| 国产成人a区在线观看| 人妻夜夜爽99麻豆av| 日本猛色少妇xxxxx猛交久久| 乱系列少妇在线播放| 80岁老熟妇乱子伦牲交| 国产精品无大码| 超碰av人人做人人爽久久| 热re99久久精品国产66热6| 亚洲,欧美,日韩| 亚洲真实伦在线观看| 亚洲国产精品一区三区| 欧美丝袜亚洲另类| 少妇高潮的动态图| 18禁裸乳无遮挡动漫免费视频| 99久国产av精品国产电影| 一级爰片在线观看| 男女边摸边吃奶| 丰满乱子伦码专区| 亚洲av综合色区一区| 国产欧美亚洲国产| 日韩精品有码人妻一区| 春色校园在线视频观看| 99久久精品一区二区三区| 2022亚洲国产成人精品| 欧美成人a在线观看| 多毛熟女@视频| 精品人妻熟女av久视频| 不卡视频在线观看欧美| 一本—道久久a久久精品蜜桃钙片| 高清欧美精品videossex| 亚洲人成网站高清观看| 免费人妻精品一区二区三区视频| 免费大片18禁| 尤物成人国产欧美一区二区三区| 成年美女黄网站色视频大全免费 | 精品国产三级普通话版| 欧美xxxx黑人xx丫x性爽| 国产深夜福利视频在线观看| 制服丝袜香蕉在线| 国产视频首页在线观看| 美女视频免费永久观看网站| 久久人妻熟女aⅴ| 有码 亚洲区| 狂野欧美激情性bbbbbb| 人人妻人人爽人人添夜夜欢视频 | 亚洲综合色惰| 水蜜桃什么品种好| 免费大片黄手机在线观看| www.色视频.com| 我要看黄色一级片免费的| 亚洲第一av免费看| 久久国产乱子免费精品| 美女cb高潮喷水在线观看| 欧美一级a爱片免费观看看| 国内少妇人妻偷人精品xxx网站| 一级毛片电影观看| 蜜桃久久精品国产亚洲av| 欧美日韩一区二区视频在线观看视频在线| 国产精品欧美亚洲77777| 韩国高清视频一区二区三区| 十分钟在线观看高清视频www | 国产免费一区二区三区四区乱码| 亚洲一区二区三区欧美精品| 免费大片黄手机在线观看| 成人美女网站在线观看视频| 男人爽女人下面视频在线观看| 亚洲欧美中文字幕日韩二区| 老司机影院成人| 麻豆成人午夜福利视频| 大片免费播放器 马上看| 欧美精品国产亚洲| 欧美日韩视频精品一区| 精品国产一区二区三区久久久樱花 | 在线观看一区二区三区| 久久精品久久久久久噜噜老黄| 久久精品国产自在天天线| 久久久久精品久久久久真实原创| 国产精品一区二区三区四区免费观看| 国产亚洲av片在线观看秒播厂| a级毛色黄片| 18禁裸乳无遮挡免费网站照片| 综合色丁香网| 中文精品一卡2卡3卡4更新| 黄色欧美视频在线观看| 一本久久精品| 免费观看a级毛片全部| 日韩欧美 国产精品| 国产亚洲午夜精品一区二区久久| 香蕉精品网在线| 国产精品99久久久久久久久| 少妇人妻久久综合中文| 下体分泌物呈黄色| 亚洲欧美日韩东京热| 99久久精品热视频| 春色校园在线视频观看| 少妇的逼水好多| 亚洲美女黄色视频免费看| 久久97久久精品| 欧美高清性xxxxhd video| 九九在线视频观看精品| 男女国产视频网站| 熟妇人妻不卡中文字幕| 国产高潮美女av| 一级a做视频免费观看| 久久久久国产精品人妻一区二区| 精品酒店卫生间| 我的女老师完整版在线观看| 成年免费大片在线观看| 在线天堂最新版资源| 少妇的逼好多水| 亚洲国产色片| 国产av码专区亚洲av| 亚洲精品国产av蜜桃| 视频中文字幕在线观看| 国产欧美另类精品又又久久亚洲欧美| 日韩精品有码人妻一区| 亚洲一区二区三区欧美精品| 天天躁夜夜躁狠狠久久av| 久久ye,这里只有精品| 免费看日本二区| 亚洲成色77777| 亚州av有码| 老司机影院成人| 国产成人一区二区在线| 爱豆传媒免费全集在线观看| 欧美97在线视频| 国产免费福利视频在线观看| 黄色怎么调成土黄色| 99久久人妻综合| 搡女人真爽免费视频火全软件| 男人添女人高潮全过程视频| 我的女老师完整版在线观看| 一级毛片黄色毛片免费观看视频| 亚洲精品一区蜜桃| 少妇熟女欧美另类| 国产 精品1| 亚洲精品成人av观看孕妇| 99久久精品国产国产毛片| 久久人人爽人人片av| 成人免费观看视频高清| 成人特级av手机在线观看| 国产91av在线免费观看| 91午夜精品亚洲一区二区三区| 久久久久久久精品精品| av黄色大香蕉| av播播在线观看一区| 国产免费视频播放在线视频| 美女视频免费永久观看网站| 在线看a的网站| 亚洲av电影在线观看一区二区三区| 晚上一个人看的免费电影| 日韩一区二区三区影片| 日韩精品有码人妻一区| 亚洲第一av免费看| 国产人妻一区二区三区在| 偷拍熟女少妇极品色| 亚洲激情五月婷婷啪啪| kizo精华| 国产一区二区三区综合在线观看 | 成年人午夜在线观看视频| 在线精品无人区一区二区三 | 国产淫语在线视频| 又大又黄又爽视频免费| 97精品久久久久久久久久精品| 国产欧美亚洲国产| 一级毛片电影观看| 国产高清三级在线| 国产一区二区在线观看日韩| 日韩一区二区三区影片| 亚洲精品aⅴ在线观看| 校园人妻丝袜中文字幕| 国产av一区二区精品久久 | 国产人妻一区二区三区在| 岛国毛片在线播放| 美女xxoo啪啪120秒动态图| 国产 一区 欧美 日韩| 亚洲av国产av综合av卡| 亚洲av免费高清在线观看| 麻豆成人av视频| 亚洲国产欧美在线一区| 亚洲美女搞黄在线观看| 国产黄频视频在线观看| 国产片特级美女逼逼视频| 国产精品一区二区三区四区免费观看| 亚洲国产精品一区三区| 久久这里有精品视频免费| 婷婷色综合大香蕉| 日韩一区二区三区影片| 国产爱豆传媒在线观看| 18禁裸乳无遮挡免费网站照片| 久久av网站| 国产一区亚洲一区在线观看| 女性被躁到高潮视频| 亚洲av成人精品一二三区| 国产色婷婷99| 一二三四中文在线观看免费高清| 亚洲国产高清在线一区二区三| 免费播放大片免费观看视频在线观看| 大片免费播放器 马上看| 亚洲精品乱码久久久v下载方式| 精品酒店卫生间| 看非洲黑人一级黄片| 亚洲国产精品一区三区| 久久久久久久国产电影| 性高湖久久久久久久久免费观看| 亚洲一级一片aⅴ在线观看| 插阴视频在线观看视频| 国产深夜福利视频在线观看| 免费观看在线日韩| 亚洲综合色惰| 国产淫片久久久久久久久| 青春草视频在线免费观看| 午夜福利在线观看免费完整高清在| 亚洲,欧美,日韩| 简卡轻食公司| 日本爱情动作片www.在线观看| 久久久久视频综合| 国产欧美另类精品又又久久亚洲欧美| 毛片一级片免费看久久久久| 欧美3d第一页| 久久这里有精品视频免费| 亚洲真实伦在线观看| 久久国内精品自在自线图片| 成人亚洲欧美一区二区av| 婷婷色综合大香蕉| 国产成人91sexporn| 岛国毛片在线播放| 中文字幕av成人在线电影| 国产一区二区在线观看日韩| 深夜a级毛片| 少妇的逼好多水| 一本久久精品| 亚洲人成网站在线播| 久热这里只有精品99| 亚洲欧洲国产日韩| 18禁动态无遮挡网站| 久久久午夜欧美精品| 亚洲国产色片| 亚洲av欧美aⅴ国产| 日韩欧美精品免费久久| 夫妻性生交免费视频一级片| 少妇猛男粗大的猛烈进出视频| 欧美人与善性xxx| 日本猛色少妇xxxxx猛交久久| 国产免费又黄又爽又色| 我的女老师完整版在线观看| 日日啪夜夜爽| 高清日韩中文字幕在线| 伊人久久国产一区二区| 亚洲av.av天堂| 在线亚洲精品国产二区图片欧美 | 欧美3d第一页| 久久毛片免费看一区二区三区| 国产精品国产三级国产专区5o| 国产精品久久久久久精品古装| 秋霞伦理黄片| 国产亚洲5aaaaa淫片| 丰满人妻一区二区三区视频av| 深爱激情五月婷婷| 国产乱来视频区| 亚洲精品aⅴ在线观看| 我要看黄色一级片免费的| 国产男女超爽视频在线观看| 亚洲国产最新在线播放| 人体艺术视频欧美日本| 综合色丁香网| 日韩电影二区| 亚洲av成人精品一二三区| 国产淫片久久久久久久久| 人人妻人人爽人人添夜夜欢视频 | 国产成人午夜福利电影在线观看| 免费播放大片免费观看视频在线观看| 在线免费十八禁| 日韩国内少妇激情av| 亚洲综合色惰| 一级av片app| 九九在线视频观看精品| 黄色怎么调成土黄色| 99热这里只有是精品50| 一区在线观看完整版| 一本久久精品| 看免费成人av毛片| 激情五月婷婷亚洲| videos熟女内射| 国产亚洲5aaaaa淫片| 卡戴珊不雅视频在线播放| 国产亚洲av片在线观看秒播厂| 亚洲精品乱码久久久久久按摩| 在线观看美女被高潮喷水网站| 老熟女久久久| 在线观看免费日韩欧美大片 | 我的老师免费观看完整版| 青春草亚洲视频在线观看| 蜜桃久久精品国产亚洲av| 国产免费一级a男人的天堂| 日韩强制内射视频| 在线亚洲精品国产二区图片欧美 | 亚洲国产成人一精品久久久| 22中文网久久字幕| 国产精品99久久99久久久不卡 | 免费看光身美女| 综合色丁香网| 大香蕉久久网| 国产免费一区二区三区四区乱码| 亚洲国产高清在线一区二区三| 久久国内精品自在自线图片| 欧美精品人与动牲交sv欧美| 水蜜桃什么品种好| 亚洲av国产av综合av卡| 亚洲成人一二三区av| 少妇人妻一区二区三区视频| 亚洲av不卡在线观看| 精品国产三级普通话版| 亚洲国产精品国产精品| av又黄又爽大尺度在线免费看| 黄色欧美视频在线观看| av一本久久久久| 国产午夜精品一二区理论片| 成人漫画全彩无遮挡| 久久亚洲国产成人精品v| 超碰av人人做人人爽久久| 伦精品一区二区三区| 免费观看性生交大片5| 国产欧美亚洲国产| 亚洲国产最新在线播放| 三级国产精品欧美在线观看| 国产 精品1| 午夜日本视频在线| 欧美丝袜亚洲另类| 黄片无遮挡物在线观看| 亚洲av不卡在线观看| 一区二区av电影网| 老女人水多毛片| 久久久久视频综合| 欧美变态另类bdsm刘玥| 国产精品女同一区二区软件| 欧美老熟妇乱子伦牲交| 高清不卡的av网站| 国产高清不卡午夜福利| 国产片特级美女逼逼视频| 国产亚洲精品久久久com| 国产白丝娇喘喷水9色精品| videossex国产| 九九在线视频观看精品| 黄色视频在线播放观看不卡| 只有这里有精品99| 久久久久久久国产电影| 亚洲精品乱久久久久久| 欧美变态另类bdsm刘玥| 久久青草综合色| 一个人看的www免费观看视频| 成人无遮挡网站| 久久人妻熟女aⅴ| 熟女人妻精品中文字幕| 成人黄色视频免费在线看| 蜜桃久久精品国产亚洲av| 色吧在线观看| a级一级毛片免费在线观看| 亚洲国产精品一区三区| 久久久久久久久久久免费av| 毛片女人毛片| 国产乱人视频| 亚洲av综合色区一区| 亚洲美女视频黄频| 久久久久久久亚洲中文字幕| 免费av中文字幕在线| 人人妻人人爽人人添夜夜欢视频 | 99久久精品国产国产毛片| 亚洲av欧美aⅴ国产| 天堂俺去俺来也www色官网| 久久精品国产自在天天线| 国产久久久一区二区三区| av播播在线观看一区| 美女cb高潮喷水在线观看| 中文字幕精品免费在线观看视频 | 国产探花极品一区二区| 久久人人爽人人爽人人片va| 精品亚洲乱码少妇综合久久| 亚洲欧美成人综合另类久久久| 欧美成人精品欧美一级黄| 自拍偷自拍亚洲精品老妇| 少妇 在线观看| 一区二区三区精品91| 国产无遮挡羞羞视频在线观看| 汤姆久久久久久久影院中文字幕| 99热全是精品| 免费高清在线观看视频在线观看| 欧美xxxx黑人xx丫x性爽| 久久精品国产亚洲av涩爱| 欧美性感艳星| 在线亚洲精品国产二区图片欧美 | 99久久人妻综合| 日本色播在线视频| 性色av一级| 蜜臀久久99精品久久宅男| 最近的中文字幕免费完整| 成人毛片a级毛片在线播放| 春色校园在线视频观看| 国产av精品麻豆| 国产av国产精品国产| 欧美日本视频| 国产精品一区二区在线不卡| 蜜桃在线观看..| 丰满乱子伦码专区| 人妻 亚洲 视频| 啦啦啦在线观看免费高清www| 老熟女久久久| 26uuu在线亚洲综合色| 特大巨黑吊av在线直播| 日韩精品有码人妻一区| 国产久久久一区二区三区| 九九久久精品国产亚洲av麻豆| 极品教师在线视频| 深夜a级毛片| 免费看av在线观看网站| 日本色播在线视频| 久久久久久久国产电影| 我的女老师完整版在线观看| 欧美日本视频| 欧美日韩国产mv在线观看视频 | 成人二区视频| 高清日韩中文字幕在线| 少妇的逼水好多| 久久97久久精品| 三级国产精品欧美在线观看| 极品教师在线视频| 一级片'在线观看视频| 不卡视频在线观看欧美| 久久国内精品自在自线图片| 国产精品无大码| av在线app专区| 免费av中文字幕在线| 看十八女毛片水多多多| 51国产日韩欧美| 蜜臀久久99精品久久宅男| 久久精品国产亚洲网站| 国产精品国产三级专区第一集| 久久久久网色| 亚洲欧美一区二区三区黑人 | 国精品久久久久久国模美| 国产男女超爽视频在线观看| 2021少妇久久久久久久久久久| 久久久久久九九精品二区国产| 中文字幕精品免费在线观看视频 | 精品国产露脸久久av麻豆| 精品久久久久久久末码| 岛国毛片在线播放| 最近中文字幕高清免费大全6| 久久av网站| 噜噜噜噜噜久久久久久91| 国产淫语在线视频| 各种免费的搞黄视频| 久久久久久人妻| 久久国产乱子免费精品| 三级国产精品片| 午夜福利在线在线| 国产黄色视频一区二区在线观看| av在线蜜桃| 久久国内精品自在自线图片| 欧美日韩综合久久久久久| 91精品国产国语对白视频| 色综合色国产| 亚洲色图综合在线观看| 国产精品99久久久久久久久| 久久综合国产亚洲精品| 午夜免费男女啪啪视频观看| 97在线视频观看| 80岁老熟妇乱子伦牲交| 国产精品三级大全| 少妇的逼水好多| 狂野欧美激情性xxxx在线观看| 美女中出高潮动态图| 午夜激情久久久久久久| 亚洲精品一二三| 啦啦啦视频在线资源免费观看| 成人高潮视频无遮挡免费网站| 午夜免费鲁丝| 日韩成人伦理影院| 国产成人免费无遮挡视频| 国产乱人视频| 新久久久久国产一级毛片| 天堂8中文在线网| 亚洲欧美精品自产自拍| 国产v大片淫在线免费观看| 有码 亚洲区| 一个人看的www免费观看视频| 久久精品国产亚洲网站| 亚洲自偷自拍三级| 亚洲中文av在线| 久久精品国产亚洲网站| 国产一区亚洲一区在线观看| 黄色一级大片看看| 一级黄片播放器| 久久久久视频综合| 国产在线免费精品| 国产美女午夜福利| 日本vs欧美在线观看视频 | 国产精品成人在线| 国产精品麻豆人妻色哟哟久久| 久久6这里有精品| 在线观看av片永久免费下载| 亚洲精品一区蜜桃| 色视频在线一区二区三区| 国产女主播在线喷水免费视频网站| 久久精品国产亚洲av天美| 亚洲av电影在线观看一区二区三区| 永久网站在线| 亚洲精品乱码久久久v下载方式| 亚洲av电影在线观看一区二区三区| 国产免费福利视频在线观看| 亚洲美女搞黄在线观看| 国产精品女同一区二区软件| 交换朋友夫妻互换小说| 老熟女久久久| 日韩成人av中文字幕在线观看| 天天躁夜夜躁狠狠久久av| 免费黄频网站在线观看国产|