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Object-to-camera motion produces a variety of apparent motion patterns thatsignificantly affect performance of short-term visual trackers. Despite beingcrucial for designing robust trackers, their influence is poorly explored instandard benchmarks due to weakly defined, biased and overlapping attributeannotations. In this paper we propose to go beyond pre-recorded benchmarks withpost-hoc annotations by presenting an approach that utilizes omnidirectionalvideos to generate realistic, consistently annotated, short-term trackingscenarios with exactly parameterized motion patterns. We have created anevaluation system, constructed a fully annotated dataset of omnidirectionalvideos and the generators for typical motion patterns. We provide an in-depthanalysis of major tracking paradigms which is complementary to the standardbenchmarks and confirms the expressiveness of our evaluation approach.

Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking