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Tracking social groups within and across cameras

Abstract: We propose a method for tracking groups from single and multiple cameras with disjoint fields of view. Our formulation follows the tracking-by-detection paradigm where groups are the atomic entities and are linked over time to form long and consistent trajectories. To this end, we formulate the problem as a supervised clustering problem where a Structural SVM classifier learns a similarity measure appropriate for group entities. Multi-camera group tracking is handled inside the framework by adopting an orthogonal feature encoding that allows the classifier to learn inter- and intra-camera feature weights differently. Experiments were carried out on a novel annotated group tracking data set, the DukeMTMC-Groups data set. Since this is the first data set on the problem it comes with the proposal of a suitable evaluation measure. Results of adopting learning for the task are encouraging, scoring a +15% improvement in F1 measure over a non-learning based clustering baseline. To our knowledge this is the first proposal of this kind dealing with multi-camera group tracking.


Solera, Francesco; Calderara, Simone; Ristani, Ergys; Tomasi, Carlo; Cucchiara, Rita "Tracking social groups within and across cameras" IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, vol. 27, pp. 441 -453 , 2017 DOI: 10.1109/TCSVT.2016.2607378

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