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.

本文介绍了一种多视角轨迹对比学习的策略，通过建立动态更新的内存库来维护整个轨迹的向量，使用轨迹级对比损失探索全局信息，同时在推断阶段，开发了一种相似度引导的特征融合策略来进一步提高轨迹表示的质量，实验结果表明，本方法已经超越了先前的跟踪器，并取得了最新的表现。