Split-and-match: A Bayesian framework for vehicle re-identification in road tunnels

Vehicle re-identification is key to keep track of vehicles monitored by a multicamera network with non-overlapping views. In this paper, we propose a probabilistic framework based on a two-step strategy that re-identifies vehicles in road tunnels. The first step consists of splitting the re-identification problem by connecting groups of vehicles observed in different cameras using certain motion and appearance criteria. In the second step, we build a Bayesian model that finds the optimal assignment between vehicles of connected groups. Descriptors like trace transform signatures, lane change, and motion discrepancies are used to derive our probabilistic framework. Experimental tests reveal that connected groups derived from the first step are composed of 4 vehicles on average. This allow us to constrain the number of candidate matches and increase the chances of getting the correct match. In the second step, our Bayesian model succeeds in matching vehicles among candidates with very similar appearance and under uneven illumination conditions. In general, our system reports a reidentification accuracy of 92% using a nearest-neighbor matcher, and 98% using a one-to-one matcher. These results outperform previous works and encourage us to further develop our solution for other re-identification applications.