Real-Time Human Detection using Relational Depth Similarity Features
- Sho Ikemura, Hironobu Fujiyoshi
- Asian Conference on Computer Vision, pp. 1–14, 2010
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Many conventional human detection methods use features based on gradients, such as histograms of oriented gradients (HOG), but human occlusions and complex backgrounds make accurate human detection difficult. Furthermore, real-time processing also presents problems because the use of raster scanning while varying the window scale comes at a high computational cost. To overcome these problems, we propose a method for detecting humans by Relational Depth Similarity Features(RDSF) based on depth information obtained from a TOF camera. Our method calculates the features derived from a similarity of depth histograms that represent the relationship between two local regions. During the process of detection, by using raster scanning in a 3D space, a considerable increase in speed is achieved. In addition, we perform highly accurate classification by considering of occlusion regions. Our method achieved a detection rate of 95.3% with a false positive rate of 1.0%. It also had a 11.5% higher performance than the conventional method, and our detection system can run in real-time (10 fps).