A hierarchical relaxed partitioning system (HRPS) is proposed for recognizing similar activities which has a feature space with multiple overlaps. Two feature descriptors are built from the human motion analysis of a 2-D stick figure to represent cyclic and noncyclic activities. The HRPS first discerns the pure and impure activities, i.e., with no overlaps and multiple overlaps in the feature space, respectively, then tackles the multiple overlaps problem of the impure activities via an innovative majority voting scheme. The results show that the proposed method robustly recognizes various activities of two different resolution data sets, i.e., low and high (with different views). The advantage of HRPS lies in the real-time speed, ease of implementation and extension, and nonintensive training.

In this paper, a method is presented to label and track anatomical landmarks (e.g., head, hand/arm, feet), which are referred to as significant body points (SBPs), using implicit body models. By considering the human body as an inverted pendulum model, ellipse fitting and contour moments are applied to classify it as being in Stand, Sit, or Lie posture. A convex hull of the silhouette contour is used to determine the locations of SBPs. The particle filter or a motion flow-based method is used to predict SBPs in occlusion. Stick figures of various activities are generated by connecting the SBPs. The qualitative and quantitative evaluation show that the proposed method robustly labels and tracks SBPs in various activities of two different (low and high) resolution data sets.