The invention relates to an ATM self-service business hall behavior
analysis method based on depth information. Behavior analysis based on common two-dimensional cameras is adopted in the prior art, and the situation that targets cannot be accurately positioned exists. According to the ATM self-service business hall behavior
analysis method, firstly, binocular cameras are adopted for serving as acquisition equipment for the depth information, background modeling is carried out on depth maps, a
Gaussian mixture model of each pixel is learned and updated, and
background distribution is determined; secondly, the probability value of each pixel in each new
depth map is worked out, and clustering segmentation is carried out on a front
depth map according to the DENCLUE
algorithm; finally, pixels in each
human body region in each camera are projected onto the ground, association of projected targets is carried out by using multi-camera
offset calibration, and therefore target detection under the multiple cameras is achieved under a
global coordinate system. The depth information is used and background modeling of RGB information is combined, so that the stability of target detection is greatly improved, and a good basis is provided for following behavior analysis.