Abstract
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.
Highlights
Camera networks for surveillance applications play a key role to ensure safety of public gatherings [1,2,3,4]
We introduce a novel decentralised approach based on reinforcement learning (RL) which allows every camera to learn how to optimise the coverage performances
We present the quantitative results obtained with our 4 different approaches in the simulated environment
Summary
Camera networks for surveillance applications play a key role to ensure safety of public gatherings [1,2,3,4]. Security applications in crowded scenarios have to deal with a variety of factors which can lead to critical situations [5,6,7]. In such scenarios, a camera network must be able to record local events as well as to ensure a global coverage of the area of interest [8]. A camera network must be able to record local events as well as to ensure a global coverage of the area of interest [8] Ensuring both coverage of the whole monitoring area and a good video quality of moving individuals is challenging using non-reconfigurable (fixed) cameras [5,9]. Fixed cameras, especially the ones with a large field of view (FoV) or a fisheye lens, would capture areas of the scene where pedestrians are not present, creating an excessive amount of irrelevant data
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