Tracking occluded Objects using partial observation

This paper presents a framework for multi-object tracking from a single fixed camera. The potential objects to track are detected with intensity-plus-chromaticity mixture models. The region-based representations of each object are tracked and predicted using a Kalman filter. A scene model is created to help predict and interpret the occluded or exiting objects. Unlike the traditional blind tracking during occlusion, the object states are estimated using partial observations whenever available. The observability of each object depends on the predictive measurement of the object, the foreground region measurement, and the scene model. This makes the algorithm more robust in terms of both qualitative and quantitative criteria.