| Summary: | Multiple Object Tracking (MOT) is the problem that involves following the trajectory of multiple objects in a sequence, generally a video. Pedestrians are among the most interesting subjects to track and recognize for many purposes such as surveillance, and safety. In recent years, Unmanned Aerial Vehicles (UAV’s) have been viewed as a viable option for monitoring public areas, as they provide a low-cost method of data collection while covering large and difficult-to-reach areas. In this paper, we present an online pedestrian tracking and re-identification framework based on learning a compact directional statistic distribution (von-Mises-Fisher distribution) for each person ID using a deep convolutional neural network. The distribution characteristics are trained to be invariant to clothes appearances and to transformations including rotation, translation, and background changes. Learning a vMF for each ID helps simultaneously in measuring the similarity between object instances and re-identifying the pedestrian’s ID. We experimentally validated our framework on standard publicly available dataset, which we used as a case study.
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