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.
@article{arxiv.2203.03796,
title = {PAMI-AD: An Activity Detector Exploiting Part-attention and Motion Information in Surveillance Videos},
author = {Yunhao Du and Zhihang Tong and Junfeng Wan and Binyu Zhang and Yanyun Zhao},
journal= {arXiv preprint arXiv:2203.03796},
year = {2022}
}