Simple Cues Lead to a Strong Multi-Object Tracker

• URL: http://arxiv.org/abs/2206.04656v7
• Date: Wed, 26 Apr 2023 09:44:03 GMT
• Title: Simple Cues Lead to a Strong Multi-Object Tracker
• Authors: Jenny Seidenschwarz, Guillem Bras\'o, Victor Castro Serrano, Ismail Elezi, and Laura Leal-Taix\'e
• Abstract summary: We propose a new type of tracking-by-detection (TbD) for Multi-Object Tracking. We show that a combination of our appearance features with a simple motion model leads to strong tracking results. Our tracker generalizes to four public datasets, namely MOT17, MOT20, BDD100k, and DanceTrack, achieving state-of-the-art performance.
• Score: 3.7189423451031356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For a long time, the most common paradigm in Multi-Object Tracking was tracking-by-detection (TbD), where objects are first detected and then associated over video frames. For association, most models resourced to motion and appearance cues, e.g., re-identification networks. Recent approaches based on attention propose to learn the cues in a data-driven manner, showing impressive results. In this paper, we ask ourselves whether simple good old TbD methods are also capable of achieving the performance of end-to-end models. To this end, we propose two key ingredients that allow a standard re-identification network to excel at appearance-based tracking. We extensively analyse its failure cases, and show that a combination of our appearance features with a simple motion model leads to strong tracking results. Our tracker generalizes to four public datasets, namely MOT17, MOT20, BDD100k, and DanceTrack, achieving state-of-the-art performance. https://github.com/dvl-tum/GHOST.

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