Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. On the other hand, learning-based methods are usually based on simpler features, and are trained on pairs of cameras to discriminate between individuals. In this paper, we present a method that joins these two ideas: given an arbitrary stateof-the-art set of features, no matter their number, dimensionality or descriptor, the proposed multi-class learning approach learns how to fuse them, ensuring that the features agree on the classification result. The approach consists of a semi-supervised multi-feature learning strategy, that requires at least a single image per person as training data. To validate our approach, we present results on different datasets, using several heterogeneous features, that set a new level of performance in the person re-identification problem.
Abstract-This paper introduces a testbed for sensor and robot network systems, currently composed of 10 cameras and 5 mobile wheeled robots equipped with several sensors for self-localization, obstacle avoidance and vision cameras, and wireless communications. The testbed includes a serviceoriented middleware to enable fast prototyping and implementation of algorithms previously tested in simulation, as well as to simplify integration of subsystems developed by different partners. We survey an integrated approach to human-robot interaction that has been developed supported by the testbed under an European research project. The application integrates innovative methods and algorithms for people tracking and waving detection, cooperative perception among static and mobile cameras to improve people tracking accuracy, as well as decision-theoretical approaches to sensor selection and task allocation within the sensor network.
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