Multidimensional velocity filters for visual scene analysis in automotive driver assistance systems

Automotive scenery often contains objects that can be classified by object speed and movement direction. These features can be extracted from video data by linear n-D filters, which have already been analyzed in the past. While soundness of results was convincing, interest in those systems declined due to the reduced computational abilities of contemporary computers. Modern hardware allows realization of velocity filters, if the n-D system is carefully adapted to the analysis problem. The present paper analyzes the premises for application of velocity filters in the domain of automotive driver assistance systems, i.e. with respect to detectability of objects and implementability in a cost effective way. Especially the influence of the frame rate and the temporal violation of the sampling theorem are analyzed. Transfer functions for n-D filters working in a vision-based blind spot collision avoidance system are presented and discussed, and promising approaches for future application fields are proposed.