Tracking customers in crowded retail scenes with Siamese Tracker
Pha Nguyen, Son T. Tran · 2020
Object tracking is a fundamental domain in computer vision, especially multiple human tracking, which is an important task to solve many key problems, including traffic counting, behavior analysis of in-store customers. It is very difficult to handle these problems in heavily crowded scenes because of pedestrian occlusions. Recent research works focus on tracking-by-detection, which breaks tracking problem down into two steps: detect objects then associate them with groups by motion or features. Recent object detectors still do not have the accuracy-speed balance and do not work perfectly in a crowded area. In this paper, we propose a new method for human tracking, which is less depended on the instability of the object detector and inherits the success of Siamese Visual Tracking in recent years. While Siamese-family trackers work on a single object, we extend the problem by proposing a strategy that manages across multiple tracker and combines a human detector as a semi-supervisor for tracker location correction. Additionally, the detector-tracker associating strategy adapts the transformation of human poses and the acquisition of new pedestrians during the tracking process. Our method outperforms the state-of-the-art algorithms on our crowded scenes dataset.