Solutions to motion self-occlusion problem in human activity analysis

Human motion self-occlusion due to motion overlapping in the same region is a daunting task to solve. Various motion-recognition methods either bypass this problem or solve this problem in complex manner. Appearance-based template matching paradigms are simpler and hence faster approaches for activity analysis. In this paper, we concentrate on motion self-occlusion problem due to motion overlapping in various complex activities for recognition. This paper illustrates the directional motion history image concept and compares this motion representation approach with multilevel motion history representation and hierarchical motion history histogram representation to solve the self-occlusion problem of basic motion history image representation. We employ some complex aerobics and find the robustness of our method compared to other methods for this self-occlusion problem. We employ seven higher order Hu moments to compute the feature vector for each activity. Afterwards, k-nearest neighbor method is utilized for classification with leave-one-out paradigm. The comparative results clearly demonstrate the superiority of our method than other recent approaches.