-A A +A
In this paper, a computational framework for occupancy detection and profiling based exclusively on depth data is presented. 3D depth sensors offer many advantages against traditional video cameras. Occupants’ privacy can be assured more effectively because depth information is unsuitable to reveal the person's identity. Notable low-level computer vision tasks can be simplified, thus lightening the computational load. The presented framework is suitable for wall-mounting setups as well as for ceiling-mounting setups, and scales well with the number of people. To take full advantage of depth data and to accommodate specificities of crowded environments, several improvements to the standard computer vision pipeline are suggested. Firstly, the running Gaussian average background model is adapted to work with depth distances in crowded scenes. Secondly, the classical complete linkage agglomerative …
Publication date: 
1 Apr 2015
Biblio References: 
Volume: 92 Pages: 246-266
Energy and Buildings