Weakly supervised action recognition using implicit shape models

In this paper, we present a robust framework for action recognition in video, that is able to perform competitively against the state-of-the-art methods, yet does not rely on sophisticated background subtraction preprocess to remove background features. In particular, we extend the Implicit Shape Modeling (ISM) of [10] for object recognition to 3D to integrate local spatiotemporal features, which are produced by a weakly supervised Bayesian kernel filter. Experiments on benchmark datasets (including KTH [11] and Weizmann [5]) verifies the effectiveness of our approach. © 2010 IEEE