Evolving Bayesian Graph for Three-Dimensional Vehicle Model Building From Video

Abstract

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.

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

“…It is worth noting that these probabilistic relationships can be quantified (because they are usually hidden in conditional probability density functions among nodes). Thus, descriptions and reasonings of uncertainty issues can be carried out through a uniform theoretical framework [ 138 ]. Moreover, by judging whether connections among each node are undirected or directed edges, graph methods can be divided into undirected and directed graphs.…”

Understanding Dynamic Social Grouping Behaviors of Pedestrians

“…The emergence of new groups as well as the growth, split, merge, and death of old groups can occur over time. As compared to the other algorithms, modularity based approaches have been demonstrated to be the most effective in finding good partitions in an efficient manner in large networks, and they can address weighting, overlapping and evolving problems in a network [57], therefore, we adopt modularity-based approach in our work to find the social groups from tracklet interaction networks.…”

Multiperson Tracking by Online Learned Grouping Model With Nonlinear Motion Context

“…The size of a group may change dynamically as people join and leave the group, but a group of any size can always be considered as a set of elementary groups. Therefore, focusing on finding elementary groups instead of the complete group makes our approach capable of modeling flexible group evolution [11] in the real world. Note that the social group in this paper refers to a number of individuals with correlated movements and does not indicate a group of people who know each other.…”