Multi-Object Tracking Algorithm for RGB-D Images Based on Asymmetric Dual Siamese Networks

Currently, intelligent security systems are widely deployed in indoor buildings to ensure the safety of people in shopping malls, banks, train stations, and other indoor buildings. Multi-Object Tracking (MOT), as an important component of intelligent security systems, has received much attention from many researchers in recent years. However, existing multi-objective tracking algorithms still suffer from trajectory drift and interruption problems in crowded scenes, which cannot provide valuable data for managers. In order to solve the above problems, this paper proposes a Multi-Object Tracking algorithm for RGB-D images based on Asymmetric Dual Siamese networks (ADSiamMOT-RGBD). This algorithm combines appearance information from RGB images and target contour information from depth images. Furthermore, the attention module is applied to repress the redundant information in the combined features to overcome the trajectory drift problem. We also propose a trajectory analysis module, which analyzes whether the head movement trajectory is correct in combination with time-context information. It reduces the number of human error trajectories. The experimental results show that the proposed method in this paper has better tracking quality on the MICC, EPFL, and UMdatasets than the previous work.