CATrack: Condition-aware multi-object tracking with temporally enhanced appearance features

Abstract

Multiple Object Tracking (MOT) is a critical task in computer vision with a wide range of practical applications. However, current methods often use a uniform approach for associating all targets, overlooking the varying conditions of each target. This can lead to performance degradation, especially in crowded scenes with dense targets. To address this issue, we propose a novel Condition-Aware Tracking method (CATrack) to differentiate the appearance feature flow for targets under different conditions. Specifically, we propose three designs for data association and feature update. First, we develop an Adaptive Appearance Association Module (AAAM) that selects suitable track templates based on detection conditions, reducing association errors in long-tail cases like occlusions or motion blur. Second, we design an ambiguous track filtering Selective Update strategy (SU) that filters out potential low-quality embeddings. Thus, the noise accumulation in the maintained track feature will also be reduced. Meanwhile, we propose a confidence-based Adaptive Exponential Moving Average (AEMA) method for the feature state transition. By adaptively adjusting the weights of track and detection embeddings, our AEMA better preserves high-quality target features. By integrating the above modules, CATrack enhances the discriminative capability of appearance features and improves the robustness of appearance-based associations. Extensive experiments on the MOT17 and MOT20 benchmarks validate the effectiveness of the proposed CATrack. Notably, the state-of-the-art results on MOT20 demonstrate the superiority of our method in highly crowded scenarios.