Multi-object tracking (MOT) algorithms are typically classified as one-shot or two-step algorithms. The one-shot MOT algorithm is widely studied and applied due to its fast inference speed. However, one-shot algorithms include two sub-tasks of detection and re-ID, which have conflicting directions for model optimization, thus limiting tracking performance. Additionally, MOT algorithms often suffer from serious ID switching issues, which can negatively affect the tracking effect. To address these challenges, this study proposes the DETrack algorithm, which consists of feature decomposition and feature enhancement modules. The feature decomposition module can effectively exploit the differences and correlations of different tasks to solve the conflict problem. Moreover, it can effectively mitigate the competition between the detection and re-ID tasks, while simultaneously enhancing their cooperation. The feature enhancement module can improve feature quality and alleviate the problem of target ID switching. Experimental results demonstrate that DETrack has achieved improvements in multi-object tracking performance, while reducing the number of ID switching. The designed method of feature decomposition and feature enhancement can significantly enhance target tracking effectiveness.

Person images captured by surveillance cameras in real scenes often have low resolution (LR), which suffers from severe degradation in recognition performance when matched with pre-stocked high-resolution (HR) images. There are existing methods which typically employ super-resolution (SR) techniques to address the resolution discrepancy problem in person re-identification (re-ID). However, SR techniques are intended to enhance the human eye visual fidelity of images without caring about the recovery of pedestrian identity information. To cope with this challenge, we propose an orthogonal depth feature decomposition network. And we decompose pedestrian features into resolution-related features and identity-related features who are orthogonal to each other, from which we design the identity-preserving loss and resolution-invariant loss to ensure the recovery of pedestrian identity information. When compared with the SOTA method, experiments on the MLR-CUHK03 and MLR-VIPeR datasets demonstrate the superiority of our method.