Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly, remarkable progresses have been achieved. However, the existing methods tend to use various basic models (e.g, detector and embedding model), and different training or inference tricks, etc. As a result, the construction of a good baseline for a fair comparison is essential. In this paper, a classic tracker, i.e., DeepSORT, is first revisited, and then is significantly improved from multiple perspectives such as object detection, feature embedding, and trajectory association. The proposed tracker, named StrongSORT, contributes a strong and fair baseline for the MOT community. Moreover, two lightweight and plug-and-play algorithms are proposed to address two inherent "missing" problems of MOT: missing association and missing detection. Specifically, unlike most methods, which associate short tracklets into complete trajectories at high computation complexity, we propose an appearance-free link model (AFLink) to perform global association without appearance information, and achieve a good balance between speed and accuracy. Furthermore, we propose a Gaussian-smoothed interpolation (GSI) based on Gaussian process regression to relieve the missing detection. AFLink and GSI can be easily plugged into various trackers with a negligible extra computational cost (1.7 ms and 7.1 ms per image, respectively, on MOT17). Finally, by fusing StrongSORT with AFLink and GSI, the final tracker (StrongSORT++) achieves state-of-the-art results on multiple public benchmarks, i.e., MOT17, MOT20, DanceTrack and KITTI. Codes are available at https://github.com/dyhBUPT/StrongSORT and https://github.com/open-mmlab/mmtracking.
翻译:最近,多轨跟踪(MOT)吸引了越来越多的关注,因此取得了显著的进展。然而,现有的方法往往使用各种基本模型(例如探测器和嵌入模型),以及不同的培训或推断技巧等。因此,为公平比较而构建一个良好的基线至关重要。在本文中,首先重新审查了一个经典跟踪器,即DeepSORT,然后从物体探测、特征嵌入和轨迹关联等多种角度得到显著改进。拟议的跟踪器,名为SstrightSORT,为MOT社区提供了一个强大而公平的基线。此外,还提议使用两个轻量和插接和游戏算算算算算算法来解决MOT的两个固有的“漏”问题:缺失关联和缺失检测。具体地说,与大多数方法不同,将短轨与完全的轨迹(DeeptSOT)连接起来,我们提议一个没有外观信息的全球连接模式(AFLLink20ink),并实现速度和准确之间的平衡。此外,我们提议用高压-O-OTS-S-O-OT(I)最终的检测结果,可以进入不同轨道。