Rawya, A., Salah, A., Dietrich, P.
Human action recognition from the videos is one of the most attractive topics in computer vision during the last decades due to wide applications development. This research has mainly focused on learning and recognizing actions from RGB and Depth videos (RGBD). RGBD is a powerful source of data providing the aligned depth information which has great ability to improve the performance of different problems in image understanding and video processing. In this work, a novel system for human action recognition is proposed to extract distinctive spatio and temporal feature vectors for presenting the spatio-temporal evolutions from a set of training and testing video sequences of different actions. The feature vectors are computed in two steps: The first step is the motion detection from all video frames by using spatio-temporal retina model. This model gives a good structuring of video data by removing the noise and illumination variation and use to detect potentially salient areas, these areas represent the motion information of the moving object in each frame of video sequences. In the second step, because of human motion can be seen as a type of texture pattern, the local binary pattern descriptor (LBP) is used to extract features from the spatio-temporal salient areas and formulated them as a histogram to make the bag of feature vectors. To evaluate the performance of this approach, the k-means clustering and Random Forest classification is used, and our results reached superior performance in comparison with the state-of-the-art.