Modelling depth for nonparametric foreground segmentation using RGBD devices
This work addresses foreground segmentation for computer vision applications using RGBD devices, but it appears incremental as it builds on existing nonparametric methods by incorporating depth cues.
The authors tackled the problem of foreground segmentation in RGBD video by proposing a new nonparametric approach that unifies multiple information cues, including a probabilistic depth data model to handle inaccurate depth data, and introduced a new RGBD video dataset for benchmarking. Results indicated the approach could handle various practical situations and achieve good performance.
The problem of detecting changes in a scene and segmenting the foreground from background is still challenging, despite previous work. Moreover, new RGBD capturing devices include depth cues, which could be incorporated to improve foreground segmentation. In this work, we present a new nonparametric approach where a unified model mixes the device multiple information cues. In order to unify all the device channel cues, a new probabilistic depth data model is also proposed where we show how handle the inaccurate data to improve foreground segmentation. A new RGBD video dataset is presented in order to introduce a new standard for comparison purposes of this kind of algorithms. Results show that the proposed approach can handle several practical situations and obtain good results in all cases.