Distributed Bayesian Inference for Consistent Labeling of Tracked Objects in Nonoverlapping Camera Networks

One of the fundamental requirements for visual surveillance using nonoverlapping camera networks is the correct labeling of tracked objects on each camera in a consistent way, in the sense that the observations of the same object at different cameras should be assigned with the same label. In this paper, we formulate this task as a Bayesian inference problem and propose a distributed inference framework in which the posterior distribution of labeling variable corresponding to each observation is calculated based solely on local information processing on each camera and mutual information exchanging between neighboring cameras. In our framework, the number of objects presenting in the monitored region does not need to be specified beforehand. Instead, it can be determined automatically on the fly. In addition, we make no assumption about the appearance distribution of a single object, but use “similarity” scores between appearance pairs as appearance likelihood for inference. To cope with the problem of missing detection, we consider an enlarged neighborhood of each camera during inference and use a mixture model to describe the higher order spatiotemporal constraints. Finally, we demonstrate the effectiveness of our method through experiments on an indoor office building dataset and an outdoor campus garden dataset. 1. Introduction Recently, there has been increasing research interest in wide-area video surveillance based on smart camera networks with nonoverlapping Field of View (FOV). The cameras in the networks are not only able to capture video data, but also capable of local processing and mutual communication. They usually work cooperatively for discovering and understanding the behavior of some interested objects, for example, pedestrians and vehicles, in the monitored region. One of the fundamental prerequisite for achieving this goal is the correct labeling of the observations of objects captured by each camera nodes in a consistent way. That is, the observations assigned with the same label are assumed to be generated form the same object. In this paper, we assume that the detection and tracking problem within a single camera view has been already solved, and we call some interested quantities extracted from the tracked object as a virtual “observation;” see Figure 2. Consistent labeling of tracked objects in nonoverlapping camera networks is, however, a rather challenging task. First, the object’s appearance often undergoes large variations across disjoint camera views due to significant changes in view angle, lighting, background clutter,