OVTrack: Open-Vocabulary Multiple Object Tracking

• URL: http://arxiv.org/abs/2304.08408v1
• Date: Mon, 17 Apr 2023 16:20:05 GMT
• Title: OVTrack: Open-Vocabulary Multiple Object Tracking
• Authors: Siyuan Li, Tobias Fischer, Lei Ke, Henghui Ding, Martin Danelljan, Fisher Yu
• Abstract summary: OVTrack is an open-vocabulary tracker capable of tracking arbitrary object classes. It sets a new state-of-the-art on the large-scale, large-vocabulary TAO benchmark.
• Score: 64.73379741435255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional multiple object tracking (MOT) benchmarks rely only on a few object categories that hardly represent the multitude of possible objects that are encountered in the real world. This leaves contemporary MOT methods limited to a small set of pre-defined object categories. In this paper, we address this limitation by tackling a novel task, open-vocabulary MOT, that aims to evaluate tracking beyond pre-defined training categories. We further develop OVTrack, an open-vocabulary tracker that is capable of tracking arbitrary object classes. Its design is based on two key ingredients: First, leveraging vision-language models for both classification and association via knowledge distillation; second, a data hallucination strategy for robust appearance feature learning from denoising diffusion probabilistic models. The result is an extremely data-efficient open-vocabulary tracker that sets a new state-of-the-art on the large-scale, large-vocabulary TAO benchmark, while being trained solely on static images. Project page: https://www.vis.xyz/pub/ovtrack/

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