Multi-agent Based Optic Flow
In this article, the authors present a novel algorithm for computing optic flow using a multi-agent based feature point tracking method. In this multi-agent based optic flow method, feature points which are invariant to scale, orientation and illumination changes are extracted and tracked in parallel using independent agents. Each agent is run by a separate light-weight thread which can be implemented using parallel processes on a multicore processor. The agents use a Kalman filter to predict the frame to frame position of the feature points in the image, producing position and velocity data for each feature point, which can then be used to perform optic flow, while simultaneously producing feature descriptors that can be used for object recognition and stereopsis. We show that in a parallel implementation, this algorithm provides significant performance advantages over other feature point tracking object recognition methods. It therefore may provide a plausible basis for a unified computer vision architecture including optic flow, object recognition, and stereopsis.
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