Human action recognition with salient trajectories
Signal Processing
Language-motivated approaches to action recognition
The Journal of Machine Learning Research
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In this paper, we present an effective method for human action recognition using statistical models based on optical flow orientations. We compute a distribution mixture over motion orientations at each spatial location of the video sequence. The set of estimated distributions constitutes the direction model, which is used as a mid-level feature for the video sequence. We recognize human actions using a distance metric to compare the direction model of a query sequence with the direction models of training sequences. The experimentations have been performed on standard datasets and have showed promising results.