Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that...Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.展开更多
There are various walking pavements in daily life.Slip accidents will happen if required friction for safe walking is greater than available friction between the foot and ground surface.Existing researches mostly focu...There are various walking pavements in daily life.Slip accidents will happen if required friction for safe walking is greater than available friction between the foot and ground surface.Existing researches mostly focus on horizontal or slope pavements,whereas the cross-slope walkways are less.A self-developed gait friction test platform was used to test friction,distribution of plantar pressure and spatiotemporal parameters of human walking under cross-slope condition.With the increase of cross-slope angles,the mediolateral friction increases(R^(2)=0.972,P<0.001),the anterior-posterior friction shows no sig-nificant change(R^(2)=0.758,P=0.017),the normal pressure decreases(R^(2)=0.915,P=0.007),and the high foot is more prone to slip and fall than low foot.Therefore,plantar pressure distribution of both feet was shifted to left.The gait cycle was prolonged(P<0.001),swing period of both feet decreased(P=0.029)and support period increased(P=0.015)with the increase of cross-slope angle.展开更多
文摘Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.
基金National Natural Science Foundation of China,Grant/Award Number:51175149。
文摘There are various walking pavements in daily life.Slip accidents will happen if required friction for safe walking is greater than available friction between the foot and ground surface.Existing researches mostly focus on horizontal or slope pavements,whereas the cross-slope walkways are less.A self-developed gait friction test platform was used to test friction,distribution of plantar pressure and spatiotemporal parameters of human walking under cross-slope condition.With the increase of cross-slope angles,the mediolateral friction increases(R^(2)=0.972,P<0.001),the anterior-posterior friction shows no sig-nificant change(R^(2)=0.758,P=0.017),the normal pressure decreases(R^(2)=0.915,P=0.007),and the high foot is more prone to slip and fall than low foot.Therefore,plantar pressure distribution of both feet was shifted to left.The gait cycle was prolonged(P<0.001),swing period of both feet decreased(P=0.029)and support period increased(P=0.015)with the increase of cross-slope angle.
