18 research outputs found
Predictive Visual Tracking: A New Benchmark and Baseline Approach
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.Comment: 7 pages, 5 figure