Human action recognition: a framework of statistical weighted segmentation and rank correlation-based selection

Human action recognition from a video sequence has received much attention lately in the field of computer vision due to its range of applications in surveillance, healthcare, smart homes, tele-immersion, to name but a few. However, it is still facing several challenges such as human variations, occlusion, change in illumination, complex background. In this article, we consider the problems related to multiple human detection and classification using novel statistical weighted segmentation and rank correlation-based feature selection approach. Initially, preprocessing is performed on a set of frames to remove existing noise and to make the foreground maximal differentiable compared to the background. A novel weighted segmentation method is also introduced for human extraction prior to feature extraction. Ternary features are exploited including color, shape, and texture, which are later combined using serial-based features fusion method. To avoid redundancy, rank correlation-based feature selection technique is employed, which acts as a feature optimizer and leads to improved classification accuracy. The proposed method is validated on six datasets including Weizmann, KTH, Muhavi, WVU, UCF sports, and MSR action and validated based on seven performance measures. A fair comparison with existing work is also provided which proves the significance of proposed compared to other techniques.