Multiple Object Tracking Incorporating a Person Re-Identification Using Polynomial Cross Entropy Loss

Abstract

The demand for smart surveillance systems has been driven by the ubiquity of cameras in modern society. Among the crucial tasks in such systems, person re-identification (re-ID) and multiple object tracking (MOT) are paramount. Despite the common photographic challenges they share, these tasks serve distinct objectives, complicating their integration into a unified system. To be specific, most existing work lacks a comprehensive study on effectively integrating re-ID models with object trackers to achieve optimal MOT performance. A decrease in MOT performance may occur without proper calibration for the integration of both components despite using an enhanced re-ID model for the tracker. To address these issues, we propose a straightforward and effective solution that integrates an improved re-ID model into a MOT framework, the BoT-SORT tracker, ensuring enhanced MOT performance on the well-known benchmarks MOT17 and MOT20 with fewer parameters for tuning. Recognizing the sub-optimal performance of existing re-ID models with their original loss functions, we introduce a novel loss function that incorporates a polynomial cross-entropy component to enhance training efficiency on closed-world datasets. As a result, the re-ID model trained with the proposed method achieves state-of-the-art performance on Market1501 and DukeMTMC datasets, and is subsequently applied to a BoT-SORT tracker with a post-processing re-ranking module for MOT. Experimental results show that the proposed method achieves 81.2% and 77.8% MOTA scores on MOT17 and MOT20 datasets, respectively, outperforming the state-of-the-art MOT methods.

Keywords

• Deep metric learning
• multiple object tracking
• person re-identification
• tracking-by-detection