Abstract
The main challenge of Multi-Object Tracking~(MOT) lies in maintaining a continuous trajectory for each target. Existing methods often learn reliable motion patterns to match the same target between adjacent frames and discriminative appearance features to re-identify the lost targets after a long period. However, the reliability of motion prediction and the discriminability of appearances can be easily hurt by dense crowds and extreme occlusions in the tracking process. In this paper, we propose a simple yet effective multi-object tracker, i.e., MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range. For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target. For extreme occlusions, we build a novel Refind Module to learn reliable long-term motions from the target's history trajectory, which can link the interrupted trajectory with its corresponding detection. Our Interaction Module and Refind Module are embedded in the well-known tracking-by-detection paradigm, which can work in tandem to maintain superior performance. Extensive experimental results on MOT17 and MOT20 datasets demonstrate the superiority of our approach in challenging scenarios, and it achieves state-of-the-art performances at various MOT metrics.
Abstract (translated)
多目标跟踪~(MOT)的主要挑战在于维持每个目标持续的跟踪轨迹。现有的方法通常学习可靠的运动模式,在相邻帧之间匹配相同的目标,并在长时间内识别丢失的目标。然而,在跟踪过程中,密集的人群和极端的遮挡可能会伤害运动预测的可靠性和外貌区分的精度。在本文中,我们提出了一种简单但有效的多目标跟踪器,即 MotionTrack,它在一个统一框架中学习可靠的短期和长期运动,将轨迹从短到长range联系起来。对于密集的人群,我们设计了一个新相互作用模块,从短期轨迹中学习相互作用 aware 的运动,可以估计每个目标的复杂运动。对于极端遮挡,我们建立了一个新找回模块,从目标的历史轨迹中学习可靠的长期运动,可以链接中断的轨迹与相应的检测。我们的相互作用模块和找回模块嵌入了著名的跟踪-检测范式,可以协同工作以保持更好的性能。在 MOT17 和 MOT20 数据集上的广泛实验结果表明,我们在挑战性场景中的优越性,并且它在不同 MOT 指标上实现了最先进的性能。
URL
https://arxiv.org/abs/2303.10404
