PAMI-AD: An Activity Detector Exploiting Part-attention and Motion Information in Surveillance Videos

Activity detection in surveillance videos is a challenging task caused by small objects, complex activity categories, its untrimmed nature, etc. In this work, we propose an effective activity detection system for person-only and vehicle-only activities in untrimmed surveillance videos, named PAMI-AD. It consists of four modules, i.e., multi-object tracking, background modeling, activity classifier and post-processing. In particular, we propose a novel part-attention mechanism for person-only activities and a simple but strong motion information encoding method for vehicle-only activities. Our proposed system achieves the best results on the VIRAT dataset. Furthermore, our team won the 1st place in the TRECVID 2021 ActEV challenge.

GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021

In recent years, algorithms for multiple object tracking tasks have benefited from great progresses in deep models and video quality. However, in challenging scenarios like drone videos, they still suffer from problems, such as small objects, camera movements and view changes. In this paper, we propose a new multiple object tracker, which employs Global Information And some Optimizing strategies, named GIAOTracker. It consists of three stages, i.e., online tracking, global link and post-processing. Given detections in every frame, the first stage generates reliable tracklets using information of camera motion, object motion and object appearance. Then they are associated into trajectories by exploiting global clues and refined through four post-processing methods. With the effectiveness of the three stages, GIAOTracker achieves state-of-the-art performance on the VisDrone MOT dataset and wins the 3rd place in the VisDrone2021 MOT Challenge.