Action recognition is very important for many applications such as video surveillance, human-computer interaction, and so on; view-invariant action recognition is hot and difficult as well in this field. In this paper, a new discriminative model is proposed for video-based view-invariant action recognition. In the discriminative model, motion pattern and view invariants are perfectly fused together to make a better combination of invariance and distinctiveness. We address a series of issues, including interest point detection in image sequence, motion feature extraction and description, and view-invariant calculation. First, motion detection is used to extract motion information from videos, which is much more efficient than traditional background modeling and tracking-based methods. Second, as for feature representation, we exact variety of statistical information from motion and view-invariant feature based on cross ratio. Last, in the action modeling, we apply a discriminative probabilistic model-hidden conditional random field to model motion patterns and view invariants, by which we could fuse the statistics of motion and projective invariability of cross ratio in one framework. Experimental results demonstrate that our method can improve the ability to distinguish different categories of actions with high robustness to view change in real circumstances.

We present a new method of computing invariants in videos captured from different views to achieve view-invariant action recognition. To avoid the constraints of collinearity or coplanarity of image points for constructing invariants, we consider several neighboring frames to compute cross ratios, namely cross ratios across frames (CRAF), as our invariant representation of action. For every five points sampled with different intervals from the trajectories of action, we construct a pair of cross ratios (CRs). Afterwards, we transform the CRs to histograms as the feature vectors for classification. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in effectiveness and stability.