Real time object tracking based on dynamic feature grouping with background subtraction

Object detection and tracking has various application areas including intelligent transportation systems. We introduce an object detection and tracking approach that combines the background subtraction algorithm and the feature tracking and grouping algorithm. We first present an augmented background subtraction algorithm which uses a low-level feature tracking as a cue. The resulting background subtraction cues are used to improve the feature detection and grouping result. We then present a dynamic multi-level feature grouping approach that can be used in real time applications and also provides high-quality trajectories. Experimental results from video clips of a challenging transportation application are presented.