PTDS CenterTrack: pedestrian tracking in dense scenes with re-identification and feature enhancement

Abstract

Multi-object tracking in dense scenes has always been a major difficulty in this field. Although some existing algorithms achieve excellent results in multi-object tracking, they fail to achieve good generalization when the application background is transferred to more challenging dense scenarios. In this work, we propose PTDS(Pedestrian Tracking in Dense Scene) CenterTrack based on the CenterTrack for object center point detection and tracking. It utilizes dense inter-frame similarity to perform object appearance feature comparisons to predict the inter-frame position changes of objects, extending CenterTrack by using only motion features. We propose a feature enhancement method based on a hybrid attention mechanism, which adds information on the temporal dimension between frames to the features required for object detection, and connects the two tasks of detection and tracking. Under the MOT20 benchmark, PTDS CenterTrack has achieved 55.6%MOTA, 55.1%IDF1, 45.1%HOTA, which is an increase of 10.1 percentage points, 4.0 percentage points, and 4.8 percentage points respectively compared to CenterTrack.