Markov Chain Monte Carlo Cascade for Camera Network Calibration Based on Unconstrained Pedestrian Tracklets

The presented work aims at tackling the problem of externally calibrating a network of cameras by observing a dynamic scene composed of pedestrians. It relies on the single assumption that human beings walk aligned with the gravity vector. Usual techniques to solve this problem involve using more assumptions such as a planar ground or assumptions about pedestrians’ motion. In this work, we drop all these assumptions and design a probabilistic layered algorithm that deals with noisy outlier-dominated hypotheses to recover the actual structure of the network. We demonstrate our process on two known public datasets and exhibit results to underline the effectiveness of our simple but adaptable approach to this general problem.

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