Human Detection by Haar-like Filtering using Depth Information
- Sho Ikemura, Hironobu Fujiyoshi
- International Conference on Pattern Recognition, pp. 813–816, 2012
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We propose a high-accuracy human detection method featuring a Haar-like filter expressing the human shape and using depth information obtained by capturing people from above with a time-of-flight (TOF) camera. This method extracts object regions by performing background subtraction against this depth information, and passes these extracted object regions through a Haar-like filter based on a human model expressing the convex shape of shoulder-head-shoulder. Human detection is achieved by integrating the results of this filtering by mean-shift clustering. The proposed method improves detection rate by 5.7% compared to a human-detection technique that simply applies meanshift clustering to depth information obtained by background subtraction. We show that our method can detect humans in real time at a frame rate of about 19 fps.