Bootstrapping Referring Multi-Object Tracking

• URL: http://arxiv.org/abs/2406.05039v1
• Date: Fri, 7 Jun 2024 16:02:10 GMT
• Title: Bootstrapping Referring Multi-Object Tracking
• Authors: Yani Zhang, Dongming Wu, Wencheng Han, Xingping Dong,
• Abstract summary: Referring multi-object tracking (RMOT) aims at detecting and tracking multiple objects following human instruction represented by a natural language expression. Our key idea is to bootstrap the task of referring multi-object tracking by introducing discriminative language words.
• Score: 14.46285727127232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring multi-object tracking (RMOT) aims at detecting and tracking multiple objects following human instruction represented by a natural language expression. Existing RMOT benchmarks are usually formulated through manual annotations, integrated with static regulations. This approach results in a dearth of notable diversity and a constrained scope of implementation. In this work, our key idea is to bootstrap the task of referring multi-object tracking by introducing discriminative language words as much as possible. In specific, we first develop Refer-KITTI into a large-scale dataset, named Refer-KITTI-V2. It starts with 2,719 manual annotations, addressing the issue of class imbalance and introducing more keywords to make it closer to real-world scenarios compared to Refer-KITTI. They are further expanded to a total of 9,758 annotations by prompting large language models, which create 617 different words, surpassing previous RMOT benchmarks. In addition, the end-to-end framework in RMOT is also bootstrapped by a simple yet elegant temporal advancement strategy, which achieves better performance than previous approaches. The source code and dataset is available at https://github.com/zyn213/TempRMOT.

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