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.

翻译：作为关键的机器人感知能力,最近对视觉跟踪进行了深入研究。在现实世界的情景中,图像流的机载处理时间不可避免地导致跟踪结果与真实世界状态之间的差异。然而,现有的视觉跟踪基准通常会让跟踪者脱线,忽视评估中的这种隐蔽性。在这项工作中,我们的目标是处理一个更现实的潜意识跟踪问题。在空中情景中,最先进的跟踪者将用新的指标共同评估跟踪准确性和效率。此外,还开发了一个新的预测视觉跟踪基线,以弥补机载计算产生的延时。我们的潜值认知基准可以更现实地评估机器人应用的跟踪者。此外,详尽的实验证明了拟议的视觉跟踪基线方法的有效性。