Vision-Based Semi-supervised Homecare with Spatial Constraint

Vision-based homecare system receives increasing research interest owing to its efficiency, portability and low-cost characters. This paper presents a vision-based semi-supervised homecare system to automatically monitor the exceptional behaviors of self-helpless persons in home environment. Firstly, our proposed framework tracks the behavior of surveilled individual using dynamic conditional random field tracker fusion, based on which we extract motion descriptor by Fourier curve fitting to model behavior routines for exception detection. Secondly, we propose a Spatial Field constraint strategy to assist SVM-based exception action decision with a Bayesian inference model. Finally, a novel semi-supervised learning mechanism is also presented to overcome the exhaustive labeling behavior in previous works. Experiments over home environment video dataset with five normal and two exceptional behavior categories shows the advantage of our proposed system comparing with previous works.