Human Action Recognition Based on Dense Sampling of Motion Boundary and Histogram of Motion Gradient

摘要

In order to realize accurate recognition of human action, feature expression of motion information is a very crucial step. Aiming at the problem that the dense sampling used for action recognition will be affected by interference factors, such as camera motion and background information redundancy, this paper proposes the human action recognition method based on dense sampling of motion boundary and motion gradient histogram. Firstly, the dense sampling strategy based on motion boundary is incorporated into the improved dense sampling to eliminate a large number of invalid sampling points and reduce the number of trajectories. Next, in order to fully excavate the internal relationship of human movement between time and space, histograms of motion gradients based on time and space derivation is introduced to capture motion information in video, which is integrated with dense features to enhance the feature expression. The experiment results on two challenging datasets show that the proposed method improves the human action recognition accuracy effectively in the case of accelerating the speed of algorithm.