Human motion analysis research, especially the human tracking part, remains a challenging task by far. The difficulties lie in several aspects: self-occlusion, high dimensionality of parameter space and the gap between high-level image understanding and low-level image features etc. In our work we use particle filter to track human movement from monocular video sequences with an articulated human body model. We fuse region, color and boundary information to build a robust measurement function. Among them, the boundary information represented by Fourier Descriptors (FD) sets up a new and effective connection between the estimated model parameters and the image likelihoods. Compared with the previously used boundary or contour cue, FD has many noticeable advantages. Moreover, we introduce an
adaptive property into the particle filter for more robust state propagation and measurement updating. Our method is shown to work effectively in experiments.
Non-invasive biometrics is of particular importance because of its application under surveillance environment. Although traditional research in this field is mostly focused on gait recognition, feature based on human body shape is one of the alternate choices we can rely on. Here we propose a body shape based identification system, trying to explore the its distinguishing power in biometrics. Robust image processing procedures such as Wiener filter are implemented to extract binary silhouettes from frontal-view human walking video. The Kalman filter, usually adopted as a powerful tool to facilitate tracking in computer vision applications, here functions as a reliable estimator to recover body shape information from the corrupted observations. The dynamically extracted static feature vectors are then compared to templates to achieve identification. We provide experimental results to demonstrate the performance of our system.