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原文传递 A Multiscale Fusion YOLOV3-Based Model for Human Abnormal Behavior Detection in Special Scenarios
题名: A Multiscale Fusion YOLOV3-Based Model for Human Abnormal Behavior Detection in Special Scenarios
正文语种: eng
作者: Zhihong Li;Jing Zhang;Yanjie Wen;Yang Dong;Wangtu Xu
作者单位: Dept. of Transportation Beijing Univ. of Civil Engineering and Architecture No. 15 Yong Yuan Rd. Daxing District Beijing 102616 China;Dept. of Transportation Beijing Univ. of Civil Engineering and Architecture No. 15 YongYuan Rd. Daxing District Beijing 102616 China;School of Traffic and Transportation Engineering Central South Univ. No. 932 South Lushan Rd. Changsha Hunan 410083 China;TravelSky Technology Limited Beijing 101300 China No. 7 Yumin Rd. Shunyi District Beijing 101300 China;School of Architecture and Civil Engineering Xiamen Univ. No. 182 University Rd. Siming District Xiamen 361005 China
关键词: Human detection; Abnormal behavior; You only look once (YOLO); Special scenarios; Crowd
摘要: Urban public security incidents are prone to occur. Better understanding of pedestrian abnormal behavior and trajectory in crowded places is conducive to crowd management and safety monitoring. A novel pedestrian abnormal behavior detection model (PABDM) is proposed to identify crowd behavior under abnormal scenarios. This model originated from a multiscale fusion you only look once (YOLO) version 3 (V3) algorithm and was trained using the PASCAL visual object classes (VOC) in combination with an abnormal pedestrian data set (APD), denoted as VOC+APD. Compared with YOLOV3-VOC, single-stage detectors (SSD)-VOC, and SSD-VOC + APD, the proposed model has notable advantages in prediction accuracy and detection efficiency. The results show that the network loss function of the model tends to be stable after 500 epochs, and its detection accuracy is 6% higher than the average accuracy of the compared models. This proposed model also effectively solves the problem of missing detection caused by edge target, fuzzy target, and small target in abnormal state human detection. The research results are of great significance for real-time crowd monitoring in complex scenes.
出版年: 2023
期刊名称: Journal of Transportation Engineering
卷: 149
期: 2
页码: 04022150.1-04022150.12
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