Predictive Visual Tracking: A New Benchmark and Baseline Approach

• URL: http://arxiv.org/abs/2103.04508v1
• Date: Mon, 8 Mar 2021 01:50:05 GMT
• Title: Predictive Visual Tracking: A New Benchmark and Baseline Approach
• Authors: Bowen Li, Yiming Li, Junjie Ye, Changhong Fu, and Hang Zhao
• Abstract summary: 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. 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.
• Score: 27.87099869398515
• License: http://creativecommons.org/licenses/by/4.0/
• 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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