Comparison of Edge-Driven Algorithms for Model-Based Motion Estimation
3D-model-based tracking offers one possibility to explicate the manner in which spatial coherence can be exploited for the analysis of image sequences. Two seemingly different approaches towards 3D-model-based tracking are compared using the same digitized video sequences of road traffic scenes. Both approaches rely on the evaluation of greyvalue discontinuities, one based on a hypothesized probability distribution function for step-discontinuities in the vicinity of model-segments, the other one based on extraction of Edge Elements (EEs) and their association to model-segments. The former approach could be considered to reflect a stronger spatial coherence assumption because the figure-of-merit function to be optimized collects evidence from all greyvalue discontinuities within a tolerance region around visible model segments. The individual association of EEs to model-segments by the alternative approach is based on a distance function which combines differences in position and orientation, thereby taking into account the gradient direction as well as the location of a local gradient maximum in gradient direction.
A detailed analysis of numerous vehicles leads to the preliminary conclusion that both approaches have different strengths and weaknesses. It turns out that the effects of how greyvalue discontinuities are taken into account are in general less important than the inclusion of Optical Flow (OF) estimates during the update-step of the current state vector for a body to be tracked. OF estimates are evaluated only within the area of the body to be tracked when projected into the image plane according to the current state estimate. Subtle effects related to simplifications and approximations during the implementation of an approach thus may influence the aggregated result of tracking numerous vehicles even in the case where spatial coherence appears to be rigorously exploited.
KeywordsOptical Flow Spatial Coherence Model Segment Edge Element Tracking Process
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