2022/03/08 by Yunhao Du, Zhihang Tong, Du, Yunhao +7
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2203.03796
openalex publication_date 2022/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Activity detection in surveillance videos is a challenging task caused by small objects, complex activity categories, its untrimmed nature, etc. Existing methods are generally limited in performance due to inaccurate proposals, poor classifiers or inadequate post-processing method. In this work, we propose a comprehensive and effective activity detection system in untrimmed surveillance videos for person-centered and vehicle-centered activities. It consists of four modules, i.e., object localizer, proposal filter, activity classifier and activity refiner. For person-centered activities, a novel part-attention mechanism is proposed to explore detailed features in different body parts. As for vehicle-centered activities, we propose a localization masking method to jointly encode motion and foreground attention features. We conduct experiments on the large-scale activity detection datasets VIRAT, and achieve the best results for both groups of activities. Furthermore, our team won the 1st place in the TRECVID 2021 ActEV challenge.