Automatic semantic parsing of the ground-plane in scenarios recorded with multiple moving cameras

Nowadays, video surveillance scenarios usually rely on manually annotated focus areas to constrain automatic video analysis tasks. Whereas manual annotation simplifies several stages of the analysis, its use hinders the scalability of the developed solutions and might induce operational problems in scenarios recorded with Multiple and Moving Cameras (MMC). To tackle these problems, an automatic method for the cooperative extraction of Areas of Interest (AoIs) is proposed. Each captured frame is segmented into regions with semantic roles using a stateof- the-art method. Semantic evidences from different junctures, cameras and points-of-view are then spatio-temporally aligned on a common ground plane. Experimental results on widely-used datasets recorded with multiple but static cameras suggest that this process provides broader and more accurate AoIs than those manually defined in the datasets. Moreover, the proposed method naturally determines the projection of obstacles and functional objects in the scene, paving the road towards systems focused on the automatic analysis of human behaviour. To our knowledge, this is the first study dealing with this problematic, as evidenced by the lack of publicly available MMC benchmarks. To also cope with this issue, we provide a new MMC dataset with associated semantic scene annotationsThis study has been partially supported by the Spanish Government through its TEC2014-53176-R HAVideo projec