Motion detection based on simulated depth measurement

Depth information is a very important cue to understand human motion. In this paper, we establish that, even with no real depth camera, the concept of obtaining depth information is applicable for human motion detection. We propose a new motion detection method based on the concept of a real world video surveillance system enhanced with depth cameras. It is developed for detecting and analysing human motion. First, it imitates depth measuring process of a depth camera. Specially chosen in the image during the initialization process, view points play the role of cameras, whereas the depth is measured as a distance from these points to the human figure in the image. Initially, the body is partitioned into four segments to obtain the information about which part of the body is moving. Then, in course of the working cycle of the method, the received depth values are constantly subtracted from the previously obtained values, and the intensity of the body motion is calculated using root mean square. The method has been tested on actions taken from a standard motion dataset (IXMAS). It proved to be stable and reliable.