Research Article
Recognition Method of Abnormal Behavior in Electric Power Violation Monitoring Video Based on Computer Vision
@INPROCEEDINGS{10.1007/978-3-031-50574-4_12, author={Mancheng Yi and Zhiguo An and Jianxin Liu and Sifan Yu and Weirong Huang and Zheng Peng}, title={Recognition Method of Abnormal Behavior in Electric Power Violation Monitoring Video Based on Computer Vision}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2024}, month={2}, keywords={Computer Vision Abnormal Behavior of Electric Power Violation Surveillance Video Identification Method Middle Figure Classification}, doi={10.1007/978-3-031-50574-4_12} }- Mancheng Yi
Zhiguo An
Jianxin Liu
Sifan Yu
Weirong Huang
Zheng Peng
Year: 2024
Recognition Method of Abnormal Behavior in Electric Power Violation Monitoring Video Based on Computer Vision
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-031-50574-4_12
Abstract
In order to improve the accuracy of abnormal behavior recognition in electric power illegal behavior monitoring, a method of abnormal behavior recognition in electric power illegal video based on computer vision is proposed. The monitoring image of electric power violations is collected by sensors, and the monitoring video image is preprocessed based on mathematical morphology and neighborhood average filtering; The static target detection method and background difference method in computer vision technology are used to separate the background and moving foreground in the video frame sequence; Locate the staff in the video image and track their movement track; Fusing FAST corner and SIFT algorithm to extract corner features and texture features of staff action behavior in the monitoring image; The above features are input into the long and short memory recurrent neural network to realize the recognition of abnormal behavior in the electric power illegal monitoring video. The results show that the Kappa coefficient between the method and the measured results remains above 80%, which proves that the recognition method improves the accuracy of abnormal behavior recognition.
