Action Recognition Based on Spatio temporal Log Euclidean Covariance Matrix
In this paper, we handle the problem of human action recognition by combining covariance matrices as local spatio temporal (ST) descriptors and local ST features extracted densely from action video. Unlike traditional methods that separately utilizing gradient based feature and optical flow based feature, we use covariance matrix to fuse the two types of feature. Since covariance matrices are Symmetric Positive Definite (SPD) matrices, which form a special type of Riemannian manifold. To measure the distance of SPDs while avoid computing the geodesic distance between them, covariance features are transformed to log Euclidean covariance matrices (LECM) by matrix logarithm operation. After encoding LECM by Locality constrained Linear Coding method, in order to provide position information to ST LECM features, spatial pyramid is used to partition the video frames, and the average pooling on absolute value function is implemented over each sub frames. Finally, non linear support vector machine is used as classifier. Experiments on public human action datasets show that the proposed method obtains great improvements in recognition accuracy, in comparison to several state ofthe art methods.