UC Riverside

Traffic videos often capture slowly changing views of moving vehicles. These different and incrementally related views provide visual cues for 3-D perception of the vehicles from 2-D videos. This paper focuses on 3-D model building of multiple vehicles with different shapes from a single generic 3-D vehicle model by incrementally accumulating evidences in streaming traffic videos collected from a single static uncalibrated camera. When we do not know a priori the class of the following vehicle to be seen (which is true in a real traffic scenario), a flexible and evolvable Bayesian graphical model (BGM) is required, where the number of nodes, the structure of links between them, and the associated conditional probability distributions can change on the fly. Current BGMs fail to provide such online flexibility. We propose a novel BGM, which is called structure-modifiable adaptive reason-building temporal Bayesian graph (SmartBG), that self-modifies in a data-driven way to model uncertainty propagation in 3-D vehicle model building from 2-D video features, where only a subset of the 2-D vehicle features is visible at any time point, e.g., out of field-of-view (entry/exit) and self-occlusion. Uncertainties are used as relative weights to fuse evidences and to compute the overall reliability of the generated models. Results for different vehicles from several traffic videos and two different viewpoints demonstrate the performance of the proposed method. © 2000-2011 IEEE.