A novel approach for autonomous detection of anomalies in crowded environments is presented in this paper. The proposed models uses a Gaussian mixture probability hypothesis density (GM-PHD) filter as feature extractor in conjunction with different Gaussian mixture hidden Markov models (GM-HMMs). Results, based on both simulated and recorded data, indicate that this method can track and detect anomalies on-line in individual crowds through multiple camera feeds in a crowded environment.
Jonas Nordlöf and Maria Andersson, "Autonomous detection of crowd anomalies in multiple-camera surveillance feeds," Proc. SPIE 9995, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII, 99950O (Presented at SPIE Security + Defence: September 27, 2016; Published: 24 October 2016); https://doi.org/10.1117/12.2241061.
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