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A probabilistic method for hierarchical 2D-3D tracking

In this paper, we present a generic way to use a hierarchical representation of prediction models for adaptive tracking purposes. Each node of the hierarchy consists of an interacting multiple models (IMM) particle filter that combines local predictions with top-down predictions arriving from nodes situated higher up in the hierarchy. Such a hierarchical prediction structure provides mechanisms to automatically control the influences between the nodes of the hierarchy. We demonstrate the gain of a hierarchical 2D-3D tracking system by first using it to track 3D elliptically rotating object in an artificial scene, where in approaching and departing phases the target is inherently hard to track in pure 2D space due to large accelerations. To the contrary, the proposed hierarchical 2D- 3D tracking system successfully tracks the target, because it benefits from the ability of dynamically adapting its prediction models. In order to test the robustness of this framework and its feasibility for real-world applications, we then show in a traffic scene that we can successfully track a motorcylist from a driving car by means of this hierarchical tracking framework.