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Proceedings Paper

Autonomous detection of crowd anomalies in multiple-camera surveillance feeds
Author(s): Jonas Nordlöf; Maria Andersson
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Paper 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.

Paper Details

Date Published: 24 October 2016
PDF: 12 pages
Proc. SPIE 9995, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII, 99950O (24 October 2016); doi: 10.1117/12.2241061
Show Author Affiliations
Jonas Nordlöf, Swedish Defence Research Agency (Sweden)
Maria Andersson, Swedish Defence Research Agency (Sweden)

Published in SPIE Proceedings Vol. 9995:
Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII
Douglas Burgess; Gari Owen; Henri Bouma; Felicity Carlysle-Davies; Robert James Stokes; Yitzhak Yitzhaky, Editor(s)

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