Multiresolution Local Autocorrelation of Optical Flows over Time for Action Recognition

We propose method for fast action recognition and comparable performance using local autocorrelation of optical flows over time. To capture action movement, dense optical flows is generated along sequence of video. Optical flows sometimes yield noise of motions that distract object of interest from another object motions and background. We suppress this by using edge based optical flow. The HOF vector is extracted from each window resolution and correlate its consecutive flow fields within cycle using local autocorrelation over time. It will gather richer information from movement while also gaining discriminative features than standard histogram methods. Comparison shows that the comparable performance is achieved over state of the arts.