A Unified Framework for Consistent 2-D/3-D Foreground Object Detection

This paper addresses 2-D and 3-D active entity detection in video scenes. Active entities are the foreground parts in a stationary background scene and they typically correspond to the regions of interest in many applications such as video surveillance, object and person tracking, and suspicious object detection, among others. We present a novel framework that permits obtaining 2-D and 3-D active entities as an inter-dependent probabilistic procedure. In the process of creating this framework, a study has been conducted to explore ways to generalize existing activity detection techniques to a Bayesian form. A new Bayesian 3-D activity detection technique has been developed. The Bayesian framework gives a unified manner to interact between the planar and the volumetric detection tasks and helps to prevent the propagation of noisy pixel observations to the 3-D space. However, when large systematic errors occur in the 2-D detection level, a different approach has to be taken to correct them. We use a new 3-D foreground detection scheme that is able to correct errors in 2-D planar detections by checking the consistency between 3-D foreground detections and the set of corresponding 2-D foreground regions.