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In this paper, we propose a novel Non-Overlapping Camera Network Tracking Dataset (CamNeT) for evaluating multi-target tracking algorithms. The dataset is composed of five to eight cameras covering both indoor and outdoor scenes at a university. This dataset consists of six scenarios. Within each scenario are challenges relevant to lighting changes, complex topographies, crowded scenes, and changing grouping dynamics. Persons with predefined trajectories are combined with persons with random trajectories. Ground truth data for predefined trajectories is provided for each camera. Also, a baseline multi-target tracking system is presented. The tracking results using the baseline system are provided, which can be compared with future works. The work provides a comprehensive multicamera dataset for performance evaluation in this challenging application domain, as well as an initial set of results.
Zhang et al. (Thu,) studied this question.
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