Journal of Pattern Recognition Research

Identification of body parts is an important first step for many tasks such as action recognition in automatic surveillance systems. In this paper, we present a body part segmentation system for image and video analysis. The proposed system utilises Hidden Markov Models and modified shape context features for statistical modeling of the human body shape. In our solution, we also demonstrate how a general and robust solution can be developed with the synthetically generated training data. The sequences of synthetically generated images are generated using three dimensional rendering and motion capture information. After the training phase, the model is used to segment silhouette images into four body parts; arms, legs, body and head. In experiments, the system is successfully used in body part segmentation, unusual activity detection in surveillance applications and arm swing detection in gait analysis. The advantages of the method include that the same model can be employed without any modifications of parameters after initial training.