Autonomous detection of crowd anomalies in multiple-camera surveillance feeds

Jonas Nordlöf; Maria Andersson

doi:10.1117/12.2241061

24 October 2016 Autonomous detection of crowd anomalies in multiple-camera surveillance feeds

Jonas Nordlöf, Maria Andersson

Proceedings Volume 9995, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII; 99950O (2016) https://doi.org/10.1117/12.2241061
Event: SPIE Security + Defence, 2016, Edinburgh, United Kingdom

Abstract

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.

Conference Presentation

Citation Download Citation

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 (24 October 2016); https://doi.org/10.1117/12.2241061

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