Online motion classification using support vector machines

We propose a motion recognition strategy that represents a videoclip as a set of filtered images, each of which encodes a short period of motion history. Given a set of videoclips whose motion types are known, a filtered image classifier is built using support vector machines. In offline classification, the label of a test videoclip is obtained by applying majority voting over its filtered images. In online classification, the most probable type of action at an instance is determined by applying the majority voting over the most recent filtered images, which are within a sliding window. The effectiveness of this strategy was demonstrated on real datasets where the videoclips were recorded using a fixed camera whose optical axis is perpendicular to the person's trajectory. In offline recognition, the proposed strategy outperforms a principal component analysis based recognition algorithm. In online recognition, the proposed strategy cannot only classify motions correctly and identify the transition between different types of motions, but also identify the existence of an unknown motion type. This latter capability and the efficiency of the proposed strategy make it possible to create a real-time motion recognition system that can not only make classifications in real-time, but also learn new types of actions and recognize them in the future.

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