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
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
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.
