In this paper we present a method for action classification in videos using trajectory features. The novelty of our approach is in formulating the problem of simultaneous detection and localization as a probabilistic chains model. In our formulation, chains are sets of regions in the video that are connected based on their joint probabilities. We describe our approach for connecting subvolumes in the video into chains, and using them as spatio-temporal detectors for actions. Our approach allows the detection and localization of multiple actions occurring simultaneously or at different locations in a single video. We test the performance of our method on two challenging action recognition datasets, and compare to state of the art methods.
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