Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking

TL;DR

This paper introduces a multi-view trajectory contrastive learning approach for online multi-object tracking, leveraging entire trajectories and multiple keypoints to enhance discriminative representations and improve tracking accuracy.

Contribution

It proposes a novel trajectory-level contrastive learning strategy with dynamic memory and multi-view keypoints, surpassing previous methods in multi-object tracking.

Findings

Achieved state-of-the-art performance on MOTChallenge.

Outperformed existing trackers in accuracy.

Demonstrated effectiveness of trajectory-level contrastive learning.

Abstract

Discriminative representation is crucial for the association step in multi-object tracking. Recent work mainly utilizes features in single or neighboring frames for constructing metric loss and empowering networks to extract representation of targets. Although this strategy is effective, it fails to fully exploit the information contained in a whole trajectory. To this end, we propose a strategy, namely multi-view trajectory contrastive learning, in which each trajectory is represented as a center vector. By maintaining all the vectors in a dynamically updated memory bank, a trajectory-level contrastive loss is devised to explore the inter-frame information in the whole trajectories. Besides, in this strategy, each target is represented as multiple adaptively selected keypoints rather than a pre-defined anchor or center. This design allows the network to generate richer representation from multiple views of the same target, which can better characterize occluded objects. Additionally, in the inference stage, a similarity-guided feature fusion strategy is developed for further boosting the quality of the trajectory representation. Extensive experiments have been conducted on MOTChallenge to verify the effectiveness of the proposed techniques. The experimental results indicate that our method has surpassed preceding trackers and established new state-of-the-art performance.