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Article

Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance

1
Department of Information Engineering and Computer Science (DISI), University of Trento, 38121 Trento, Italy
2
Institute of Networked and Embedded Systems (NES), University of Klagenfurt, 9020 Klagenfurt, Austria
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Bisagno, N.; Conci, N.; Rinner, B. Dynamic Camera Network Reconfiguration for Crowd Surveillance. In Proceedings of the 12th International Conference on Distributed Smart Cameras, Eindhoven, The Netherlands, 3–4 September 2018.
Sensors 2020, 20(17), 4691; https://doi.org/10.3390/s20174691
Received: 30 June 2020 / Revised: 3 August 2020 / Accepted: 8 August 2020 / Published: 20 August 2020
(This article belongs to the Special Issue Cooperative Camera Networks)
Crowd surveillance plays a key role to ensure safety and security in public areas. Surveillance systems traditionally rely on fixed camera networks, which suffer from limitations, as coverage of the monitored area, video resolution and analytic performance. On the other hand, a smart camera network provides the ability to reconfigure the sensing infrastructure by incorporating active devices such as pan-tilt-zoom (PTZ) cameras and UAV-based cameras, thus enabling the network to adapt over time to changes in the scene. We propose a new decentralised approach for network reconfiguration, where each camera dynamically adapts its parameters and position to optimise scene coverage. Two policies for decentralised camera reconfiguration are presented: a greedy approach and a reinforcement learning approach. In both cases, cameras are able to locally control the state of their neighbourhood and dynamically adjust their position and PTZ parameters. When crowds are present, the network balances between global coverage of the entire scene and high resolution for the crowded areas. We evaluate our approach in a simulated environment monitored with fixed, PTZ and UAV-based cameras. View Full-Text
Keywords: distributed camera network; reinforcement learning; crowd surveillance; UAV; PTZ; simulation distributed camera network; reinforcement learning; crowd surveillance; UAV; PTZ; simulation
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MDPI and ACS Style

Bisagno, N.; Xamin, A.; De Natale, F.; Conci, N.; Rinner, B. Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance. Sensors 2020, 20, 4691. https://doi.org/10.3390/s20174691

AMA Style

Bisagno N, Xamin A, De Natale F, Conci N, Rinner B. Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance. Sensors. 2020; 20(17):4691. https://doi.org/10.3390/s20174691

Chicago/Turabian Style

Bisagno, Niccolò, Alberto Xamin, Francesco De Natale, Nicola Conci, and Bernhard Rinner. 2020. "Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance" Sensors 20, no. 17: 4691. https://doi.org/10.3390/s20174691

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