Human detection by Haar-like filtering using depth information

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 mean-shift 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.