This paper addresses the problem of occluded human segmentation and then uses its results for human behavior recognition. To tackle this ill-posed problem, a novel clustering scheme is proposed for constructing a model space for posture classification. Then, a model-driven approach is proposed for separating an occluded region to individual objects. For reducing the model space, a particle filtering technique is then used for locating possible positions of each occluded object. Then, from the positions, the best model of each occluded object can be then selected using its distance maps. Then, a novel template re-projection technique is proposed for repairing an occluded object to a complete one. Due to occlusions, there will be many posture symbol converting errors in this representation. Instead of using a specific symbol, we code a posture using not only its best matched key posture but also its similarities among other key postures. With the matrix representation, different actions can be more robustly and effectively matched by comparing their KL distance.