Section: New Results

Head Detection Using RGB-D Camera

Participants : Marine Chabran, François Brémond.

keywords: RGB-D camera analysis, head detection, serious games

The goal of this work is to improve a head detection algorithm using RGB-D sensor (like a Kinect camera) for action recognition as part of a study of autism. The psychologists want to compare the learning process of children with autism syndrome depending on games (digital or physical toys).

The algorithm described in [79] represents a head by its center position. It takes three steps to determine this point :

• Determine possible head center positions using a head model : inner circle radius=6 cm, outer circle radius=20 cm (Figure 13 ).

  A good inner point is a point on the inner circle verifying :

  $depthHeadCenter+30cm>depthInnerPoint>depthHeadCenter-30cm.$

  A good outer point is a point on the outer circle verifying :

  $depthHeadCenter<depthOuterPoint+15cm.$

• Merge close head centers separated by less than 4 pixels.

• Select final head center according to its score (calculated according to the number of good inner and outer points).

For now, it works well within video where people are close to the camera (about 1 meter) and without any background just behind them (Figure 14 ).

The problem is when the person is sitting and the head is ahead of the body (Figure 15 ) or close to a wall, the difference between head depth and outer circle depth becomes not sufficient (about 10 cm).

We have evaluated the performance of this algorithm with two data sets (Table 1 ). For Lenval Hospital data set, we have evaluated 2 series of 200 frames, for the Smart Home data set, we have evaluated 3 series of 300 frames (a total of 1300 heads).