Multi-camera tracking on a graph using Markov chain Monte Carlo

Wide-area surveillance requires a system of multiple cameras that are sparsely distributed without overlapping fields of view. Tracking objects in such a setting is challenging because blind gaps between disjoint camera views cannot ensure spatial, temporal, and visual continuity in successive observations. We propose an association algorithm for tracking an unknown number of objects with sparsely distributed uncalibrated cameras. To model traffic patterns in a monitored environment, we exploit the statistics on overall traffic and the probabilistic dependence of a path in one camera view on the previous path in another camera view. The dependency and the frequency of allowable paths are represented in a graph model. Without using a high-order transition model, the proposed graph disambiguates traffic patterns and generalizes traffic constraints in motorway and indoor scenarios. Based on the graph model, we derive a posterior probability of underlying paths, given a set of observations. The posterior evaluates not only the plausibility of individual paths but also the hypothesized number of paths with respect to traffic statistics of the environment. To find the maximum a posteriori, we use Markov chain Monte Carlo (MCMC). In contrast to other MCMC-based tracking methods, the proposed MCMC sampling requires neither additional cost to compute an initial sample nor information about the number of objects passing through the environment.