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arXiv · 2103.04508

Predictive Visual Tracking: A New Benchmark and Baseline Approach

Bowen Li·Yiming Li·Junjie Ye·Changhong Fu·Hang Zhao
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Abstract

As a crucial robotic perception capability, visual tracking has been intensively studied recently. In the real-world scenarios, the onboard processing time of the image streams inevitably leads to a discrepancy between the tracking results and the real-world states. However, existing visual tracking benchmarks commonly run the trackers offline and ignore such latency in the evaluation. In this work, we aim to deal with a more realistic problem of latency-aware tracking. The state-of-the-art trackers are evaluated in the aerial scenarios with new metrics jointly assessing the tracking accuracy and efficiency. Moreover, a new predictive visual tracking baseline is developed to compensate for the latency stemming from the onboard computation. Our latency-aware benchmark can provide a more realistic evaluation of the trackers for the robotic applications. Besides, exhaustive experiments have proven the effectiveness of the proposed predictive visual tracking baseline approach.

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Bowen Li, Yiming Li, Junjie Ye, Changhong Fu, Hang Zhao. 2021-03-08. Predictive Visual Tracking: A New Benchmark and Baseline Approach. https://arxiv.org/abs/2103.04508

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