TY - GEN
T1 - Temporal Improvement of Video Traffic Anomaly Detection
T2 - 10th International Conference on Platform Technology and Service, PlatCon 2024
AU - Zhang, Xinyue
AU - Zhao, Yuxuan
AU - Man, Ka Lok
AU - Smith, Jeremy S.
AU - Jung, Young Ae
AU - Yue, Yutao
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Internet of Things (IoT) technology can provide real-time public facilities data to Intelligent Transportation Systems (ITS), especially the surveillance cameras' video data. With the popularity of video surveillance systems, Video Anomaly Detection (VAD) has become more important in society and traffic management. However, the traditional, highly manual-dependent VAD method and the neural network model-based VAD method (such as Recurrent Neural Networks, RNN) face a significant challenge in temporal stream processing. This paper analyses current specific temporal challenges and proposes a Transformer-based spatial-temporal VAD model to alleviate the influence of temporal limitations. With the development of Transformer models, global information consideration and long-term relationship building have become more accessible, and the processing of temporal information in video data has become more efficient.
AB - Internet of Things (IoT) technology can provide real-time public facilities data to Intelligent Transportation Systems (ITS), especially the surveillance cameras' video data. With the popularity of video surveillance systems, Video Anomaly Detection (VAD) has become more important in society and traffic management. However, the traditional, highly manual-dependent VAD method and the neural network model-based VAD method (such as Recurrent Neural Networks, RNN) face a significant challenge in temporal stream processing. This paper analyses current specific temporal challenges and proposes a Transformer-based spatial-temporal VAD model to alleviate the influence of temporal limitations. With the development of Transformer models, global information consideration and long-term relationship building have become more accessible, and the processing of temporal information in video data has become more efficient.
KW - Deep Learning (DL)
KW - Intelligent Transportation System (ITS)
KW - Video Anomaly Detection (VAD)
UR - http://www.scopus.com/inward/record.url?scp=85217380250&partnerID=8YFLogxK
U2 - 10.1109/PLATCON63925.2024.10830670
DO - 10.1109/PLATCON63925.2024.10830670
M3 - Conference Proceeding
AN - SCOPUS:85217380250
T3 - 2024 International Conference on Platform Technology and Service, PlatCon 2024 - Proceedings
SP - 155
EP - 159
BT - 2024 International Conference on Platform Technology and Service, PlatCon 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 August 2024 through 28 August 2024
ER -