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第36届中国控制会议

作者： Zhe Shi Songhao Zhu Yanyun Cheng Dongsheng Wang School of Automatic Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781538629185

This paper presents an approach for detecting abnormal events in videos by analyzing tracklets extracted in dense optical *** begin with,this method codes a video as a set of spatio-temporal volumes in order to obtain the contextual ***,the dense optical fields is established to extract the dense tracklets,and the descriptor histogram of oriented tracklets is utilized to quantize tracklets in terms of angle and ***,the abnormal behaviors is detected based on probability mechanism using the topology of an ensemble of *** standard approaches that use optical flow,dense trajectories have shown to be efficient for representing *** effectiveness of the proposed method is evaluated on public datasets:UCSD,subway and *** experimental results demonstrates that this method outperformed former approaches based on the dense tracklets and optical flow.

关键词： abnormal behavior detection Spatio-Temporal Volumes Histogram of Oriented Tracklets Dense Tracklets

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Lightweight abnormal behavior detection based on improved SSD algorithm

Lightweight abnormal behavior detection based on improved SS...

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第35届中国过程控制会议

作者： Xuemei Li Jin Hou Xingzhong Xiong Yan Chen Tao Wu Hao Zhou Guobin Mao Sichuan University of Science and Engineering Automation and Information Engineering Artificial Intelligence Key Laboratory of Sichuan Province

For the abnormal behavior identification problem, where the model depth is huge and cumbersome to deploy on mobile devices, a lightweight abnormal behavior detection model based on the improved SSD algorithm is proposed. Multiscale feature fusion is used to combine feature maps at different levels so that the model can capture simultaneous images;the backbone network is switched from VGGNet16 to the lighter MobileNetV3 to reduce the model size;the K-means clustering algorithm is introduced to cluster and analyze the size of the anchor frames that show anomalous behaviors, and the a priori frame sizes are corrected according to the clustering results to improve the detection speed several information scales, which strengthen the model's robustness and increase the detection performance of both small and large targets. The revised SSD model can be easily deployed on edge devices for real-time detection, as demonstrated by the experimental results, which also reveal that it has a superior detection effect for aberrant behavior detection and a smaller network structure volume.

关键词： MobileNetV3 K-means clustering abnormal behavior detection

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UAV-assisted abnormal Vehicle behavior detection in Internet of Vehicles

UAV-assisted Abnormal Vehicle Behavior Detection in Internet...

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第40届中国控制会议

作者： Qichao Xu Lifeng Zhao Zhou Su School of Mechatronic Engineering and Automation Shanghai University School of Cyber Security Xi'an Jiaotong University

With advantages of low cost, high mobility, and flexible deployment, unmanned aerial vehicle（UAVs） are employed to efficiently detect abnormal vehicle behaviors（AVBs） in the internet of vehicles（IoVs）. However, due to limited resources including battery, computing, and communication, UAVs are selfish to work cooperatively. To solve the above problem, in this paper, a game theoretical UAV incentive scheme in Io Vs is proposed. Specifically, the abnormal behavior model is first constructed, where three model categories are defined: velocity abnormality, distance abnormality, and overtaking ***, the barging pricing framework is designed to model the interactions between UAVs and IoVs, where the transaction prices are determined with the abnormal behavior category detected by UAVs. At last, simulations are conducted to verify the feasibility and effectiveness of our proposed scheme.

关键词： Internet of Vehicles(IoVs) UAVs abnormal behavior detection bargaining game

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Weak Supervised detection of abnormal behavior

Weak Supervised Detection of Abnormal Behavior

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第37届中国控制会议

作者： Songhao Zhu Xuewen Ju Xian Sun Dongsheng Wang School of Automatic Nanjing University of Posts and Telecommunications

Artificial features are adopted in most of existing abnormal behavior detection,however it is difficult to choose and design an effective behavior feature for the reason of high computation cost and complex *** solve this problem,weak supervised detection method of abnormal behavior based on temporal consistency is proposed in this ***,temporal gram matrix is constructed for a given pair of ***,a pair of behavior units（Candidate action fragment） is formed by exploiting the temporal consistency and smoothness of human behavior,which aims to locate the start and end frames of the related abnormal action and train the corresponding ***,sparse reconstruction is utilized to detect abnormal *** results conducted on CAVIAR and BOSS dataset demonstrate the effectiveness of the proposed method.

关键词： abnormal behavior detection Weak Supervised detection Temporal Consistency Sparse Reconstruction

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Review of Some Advances and Applications in Real-time High-speed Vision: Our Views and Experiences

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International Journal of Automation and computing 2016年第4期13卷 305-318页

作者： Qing-Yi Gu Idaku Ishii Robotics Laboratory Graduate School of Engineering Hiroshima University Higashi-Hiroshima Hiroshima 738-8527 Japan

The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems is limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. These high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundreds of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high Speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamics sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface, and biomedical applications.

关键词： Real-time high-speed vision target tracking abnormal behavior detection behavior mining vibration analysis 3D shapemeasurement cell sorting.

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Anomaly detection Based on Cascaded Swin Transformer

Anomaly Detection Based on Cascaded Swin Transformer

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第43届中国控制会议

作者： Yaqoob Raffay Limin Xia Syed Akram School of Automation Central South University

abnormal behavior detection in surveillance aims to identify actions that deviate from expected patterns,highlighting potential security threats or safety *** significance lies in enabling early detection and response to incidents,thus enhancing public safety and *** progress,existing algorithms often struggle with adapting to diverse environments and ensuring real-time *** research introduces a novel approach using a Swin-LSTM-based model that leverages the spatial analysis capabilities of Swin Transformers and the temporal insights of LSTM *** facilitate real-time application,the model complexity is minimized through Singular Value Decomposition(SVD) on the attention matrix and weight pruning within the MLP *** superior performance on standard datasets,this method represents a significant step forward in efficient,real-time detection of abnormal behavior.

关键词： Computer Vision abnormal behavior detection surveillance Swin Transformers LSTM real-time processing Singular Value Decomposition weight pruning

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