The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain ...The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture,so as to solve the problem of recognizing them.In response to this difficulty,this paper introduces an adjustable jump link coefficients model based on the residual network.The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior.A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper.In order to reduce the noise of the data edge,and at the same time,improve the accuracy of the data and speed up the training,a BN(Batch Normalization)layer is added before the activation function in this network.This paper trains this network model on the public ImageNet dataset,and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network.Under the same experimental conditions,compared with the original ResNet-50 model,the improved model in this paper has a 2.8%higher accuracy in recognition of abnormal behaviors on the public UTI dataset.展开更多
With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves t...With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.展开更多
Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet...Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models,this paper proposes GTB-ResNet,a network designed to detect abnormal behaviors in petroleum station staff.Design/methodology/approach-Firstly,to mitigate the issues of excessive parameters and computational complexity in 3D ResNet,a lightweight residual convolution module called the Ghost residual module(GhostNet)is introduced in the feature extraction network.Ghost convolution replaces standard convolution,reducing model parameters while preserving multi-scale feature extraction capabilities.Secondly,to enhance the model’s focus on salient features amidst wide surveillance ranges and small target objects,the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction.Lastly,to address the challenge of short time-series features leading to misjudgments in similar actions,a bidirectional gated recurrent network is added to the feature extraction backbone network.This ensures the extraction of key long time-series features,thereby improving feature extraction accuracy.Findings-The experimental setup encompasses four behavior types:illegal phone answering,smoking,falling(abnormal)and touching the face(normal),comprising a total of 892 videos.Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7%with a model parameter count of 4.46 M and a computational complexity of 3.898 G.This represents a 4.4%improvement over 3D ResNet,with reductions of 90.4%in parameters and 61.5%in computational complexity.Originality/value-Specifically designed for edge devices in oil stations,the 3D ResNet network is tailored for real-time action prediction.To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices,a lightweight residual module based on ghost convolution is developed.Additionally,to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations,a triple attention mechanism is introduced during feature extraction to enhance focus on salient features.Moreover,to overcome the potential for misjudgments arising from the similarity of actions,a Bi-GRU model is introduced to enhance the extraction of key long-term features.展开更多
基金This research was funded by the Science and Technology Department of Shaanxi Province,China,Grant Number 2019GY-036.
文摘The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture,so as to solve the problem of recognizing them.In response to this difficulty,this paper introduces an adjustable jump link coefficients model based on the residual network.The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior.A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper.In order to reduce the noise of the data edge,and at the same time,improve the accuracy of the data and speed up the training,a BN(Batch Normalization)layer is added before the activation function in this network.This paper trains this network model on the public ImageNet dataset,and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network.Under the same experimental conditions,compared with the original ResNet-50 model,the improved model in this paper has a 2.8%higher accuracy in recognition of abnormal behaviors on the public UTI dataset.
基金supported by Basic Science Research Program through the NationalResearch Foundation of Korea (NRF)funded by the Ministry of Education (2020R1A6A1A03040583).
文摘With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more important.Accordingly,it is necessary to develop a method that achieves this task using object and behavior information within video data.Existing methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a video.In this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is proposed.Objects and motions are first extracted from continuous images by combining existing DL-based image analysis models.The weight of the continuous data pattern is then analyzed through data structuring to classify the overall video.The performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action clips.The model achieved an accuracy of 0.9817,indicating excellent performance.Therefore,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.
文摘Purpose-The abnormal behaviors of staff at petroleum stations pose significant safety hazards.Addressing the challenges of high parameter counts,lengthy training periods and low recognition rates in existing 3D ResNet behavior recognition models,this paper proposes GTB-ResNet,a network designed to detect abnormal behaviors in petroleum station staff.Design/methodology/approach-Firstly,to mitigate the issues of excessive parameters and computational complexity in 3D ResNet,a lightweight residual convolution module called the Ghost residual module(GhostNet)is introduced in the feature extraction network.Ghost convolution replaces standard convolution,reducing model parameters while preserving multi-scale feature extraction capabilities.Secondly,to enhance the model’s focus on salient features amidst wide surveillance ranges and small target objects,the triplet attention mechanism module is integrated to facilitate spatial and channel information interaction.Lastly,to address the challenge of short time-series features leading to misjudgments in similar actions,a bidirectional gated recurrent network is added to the feature extraction backbone network.This ensures the extraction of key long time-series features,thereby improving feature extraction accuracy.Findings-The experimental setup encompasses four behavior types:illegal phone answering,smoking,falling(abnormal)and touching the face(normal),comprising a total of 892 videos.Experimental results showcase GTB-ResNet achieving a recognition accuracy of 96.7%with a model parameter count of 4.46 M and a computational complexity of 3.898 G.This represents a 4.4%improvement over 3D ResNet,with reductions of 90.4%in parameters and 61.5%in computational complexity.Originality/value-Specifically designed for edge devices in oil stations,the 3D ResNet network is tailored for real-time action prediction.To address the challenges posed by the large number of parameters in 3D ResNet networks and the difficulties in deployment on edge devices,a lightweight residual module based on ghost convolution is developed.Additionally,to tackle the issue of low detection accuracy of behaviors amidst the noisy environment of petroleum stations,a triple attention mechanism is introduced during feature extraction to enhance focus on salient features.Moreover,to overcome the potential for misjudgments arising from the similarity of actions,a Bi-GRU model is introduced to enhance the extraction of key long-term features.
