A Cuboid Bi-Level Log Operator for Action Classification

We propose a 3-D cuboid bi-level Laplacian-of-Gaussian (CBLoG) operator with high speed and invariant space-time scale for detecting abrupt changes of signals in videos. This 3-D CBLoG operator is applied in the detection of spatio-temporal interest points in videos. Then, we extract rich feature descriptors, including HOG, HOF, and MBH (motion boundary histogram) around the detected points, and use Fisher vector to encoding these descriptors and their combinations as action representation. In our experiments, we evaluated our recognition method on two standard data sets: KTH and UCF Sport action data sets. The best accuracy on the KTH data set achieves at 99.3%, and on the UCF Sports data set, it is 90.0%. Compared with the existing approaches, our method outperforms the state of the art tested on the KTH data set and achieves impressive performance on the UCF sports data set.