To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved...To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.展开更多
Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/u...Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events.In this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the objective.First stage,OPLKT-EMEHOs algorithm is used to generate the opticalflow from MIIs.Second stage,the MIIs opticalflow is applied as input to 3 layer CNN for detect the abnormal crowd behavior.University of Minnesota(UMN)dataset is used to evaluate the proposed system.The experi-mental result shows that the proposed method provides better classification accu-racy by comparing with the existing methods.Proposed method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.展开更多
This paper presents a model for simulating crowd evacuation and investigates three widely recognized problems. For the space continuity problem, this paper presents two computation algorithms： one uses grid space to ...This paper presents a model for simulating crowd evacuation and investigates three widely recognized problems. For the space continuity problem, this paper presents two computation algorithms： one uses grid space to evaluate the coordinates of the obstacle＇s bounding box and the other employs the geometry rule to establish individual evacuation routes. For the problem of collision, avoidance, and excess among the individuals, this paper computes the generalized force and friction force and then modifies the direction of march to obtain a speed model based on the crowd density and real time speed. For the exit selection problem, this paper establishes a method of selecting the exits by combining the exit＇s crowd state with the individuals. Finally, a particle system is used to simulate the behavior of crowd evacuation and produces useful test results.展开更多
Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd st...Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.展开更多
Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television(CCTV) cameras, it is still difficult to achieve real-time ale...Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television(CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gaborfiltered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns(signatures)is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.展开更多
The decentralization of the energy sector＇s infrastructure is commonly understood as an important step towards an efficient and sustainable energy system. However, technical measures only have a limited impact on the...The decentralization of the energy sector＇s infrastructure is commonly understood as an important step towards an efficient and sustainable energy system. However, technical measures only have a limited impact on the systems goals. The decentralization of decision-making, the empowerment of households and the occurrence of prosumer communities require more human-centric approaches to meet future challenges; the application of the subsidiarity principle and decentralization seems inevitable but also unclear in its implementation in an energy system, the cooperation behavior of community members gains in importance and the design of prosumer communities must overcome economic problems. This article emphasizes the human-centered challenges which go along with prosumer communities. We apply economic principles to reveal major problems which go with the shifts in energy-related decision-making towards prosumers and integrate behavioral science for prosumer community design. We highlight the importance of energy as an interpersonal construct; a view on energy which will gain in importance within a prosumer communities shaped network.展开更多
Studies of past accidents have revealed that various elements such as failure to identify hazards, crowd behaviors out of controlling, deficiency of the egress signage system, inconsistency between process behavior an...Studies of past accidents have revealed that various elements such as failure to identify hazards, crowd behaviors out of controlling, deficiency of the egress signage system, inconsistency between process behavior and process plan, and environmental constraints, etc. affected crowd evacuation. Above all, the human factor is the key issue in safety and disaster management, although it is bound to other factors inextricably. This paper explores crowd behaviors that may influence an urgent situation, and discusses the technique applied to the crowd prediction. Based on risk rating relative to crowd density, risk plans for different levels are proposed to dispose the potential threats. Also practical crowd management measures at different risk levels are illustrated in a case of a metro station in China. Finally, the strategies for crowd security management are advised that all stakeholders are amenable to form risk consciousness and implement safety procedures consistent with risk plans professionally and scientifically.展开更多
基金National Natural Science Foundation of China(61701029)。
文摘To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.
文摘Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events.In this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the objective.First stage,OPLKT-EMEHOs algorithm is used to generate the opticalflow from MIIs.Second stage,the MIIs opticalflow is applied as input to 3 layer CNN for detect the abnormal crowd behavior.University of Minnesota(UMN)dataset is used to evaluate the proposed system.The experi-mental result shows that the proposed method provides better classification accu-racy by comparing with the existing methods.Proposed method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.
基金supported by Shanghai Science and Technology Committee (No. 08515810200)Jiangsu Province Development Foundation (No. BS2007048)
文摘This paper presents a model for simulating crowd evacuation and investigates three widely recognized problems. For the space continuity problem, this paper presents two computation algorithms： one uses grid space to evaluate the coordinates of the obstacle＇s bounding box and the other employs the geometry rule to establish individual evacuation routes. For the problem of collision, avoidance, and excess among the individuals, this paper computes the generalized force and friction force and then modifies the direction of march to obtain a speed model based on the crowd density and real time speed. For the exit selection problem, this paper establishes a method of selecting the exits by combining the exit＇s crowd state with the individuals. Finally, a particle system is used to simulate the behavior of crowd evacuation and produces useful test results.
基金This research work is supported by the Deputyship of Research&Innovation,Ministry of Education in Saudi Arabia(Grant Number 758).
文摘Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.
基金funded by Chinese National Natural Science Foundation (No. 61671377)Shaanxi Smart City Technology Project of Xianyang (No. 2017k01-25-5)
文摘Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television(CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gaborfiltered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns(signatures)is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.
文摘The decentralization of the energy sector＇s infrastructure is commonly understood as an important step towards an efficient and sustainable energy system. However, technical measures only have a limited impact on the systems goals. The decentralization of decision-making, the empowerment of households and the occurrence of prosumer communities require more human-centric approaches to meet future challenges; the application of the subsidiarity principle and decentralization seems inevitable but also unclear in its implementation in an energy system, the cooperation behavior of community members gains in importance and the design of prosumer communities must overcome economic problems. This article emphasizes the human-centered challenges which go along with prosumer communities. We apply economic principles to reveal major problems which go with the shifts in energy-related decision-making towards prosumers and integrate behavioral science for prosumer community design. We highlight the importance of energy as an interpersonal construct; a view on energy which will gain in importance within a prosumer communities shaped network.
文摘Studies of past accidents have revealed that various elements such as failure to identify hazards, crowd behaviors out of controlling, deficiency of the egress signage system, inconsistency between process behavior and process plan, and environmental constraints, etc. affected crowd evacuation. Above all, the human factor is the key issue in safety and disaster management, although it is bound to other factors inextricably. This paper explores crowd behaviors that may influence an urgent situation, and discusses the technique applied to the crowd prediction. Based on risk rating relative to crowd density, risk plans for different levels are proposed to dispose the potential threats. Also practical crowd management measures at different risk levels are illustrated in a case of a metro station in China. Finally, the strategies for crowd security management are advised that all stakeholders are amenable to form risk consciousness and implement safety procedures consistent with risk plans professionally and scientifically.
