Transductive People Tracking in Unconstrained Surveillance
Long-term tracking of people in unconstrained scenarios is still an open problem due to the absence of constant elements in the problem setting. The camera, when active, may move and the appearance of both the background and the target may change abruptly, leading to the inadequacy of most standard tracking techniques. We propose to exploit a learning approach that considers the tracking task as a semisupervised learning problem. Given few target samples, the aim is to search for the target occurrences in the video stream, reinterpreting the problem as label propagation on a similarity graph. We propose a solution based on graph transduction that iteratively works frame by frame. In addition, to avoid drifting, we introduce an update strategy based on an evolutionary clustering technique that chooses the visual templates that better describe target appearance, evolving the model during the processing of the video. Since we model people's appearance by means of covariance matrices on color and gradient information, our framework is directly related to structure learning on Riemannian manifolds. Tests on publicly available data sets and comparisons with state-of-the-art techniques allow us to conclude that our solution exhibits interesting performances in terms of tracking precision and recall in most of the considered scenarios.