Human motion image detection and tracking method based on Gaussian mixture model combined with light sensor.

This paper focuses on a human motion image detection and tracking method that utilizes a Gaussian mixture model and light sensor. The objective is to develop an accurate and efficient approach for detecting and tracking human motion images in various applications such as human behavior analysis and intelligent monitoring. To achieve this, the method utilizes a Gaussian mixture model to establish a background model of the scene. The background model is used to estimate the static elements in the scene. By computing the background difference of the current image, a binary image of the foreground region is obtained. Additionally, data from the light sensor is incorporated to further segment and filter the foreground area, removing noise and irrelevant motion. The detection and tracking of human motion is realized through trajectory analysis and matching techniques. Experimental evaluation confirms the effectiveness and accuracy of the proposed method. The results demonstrate that the method achieves high accuracy and robustness in human motion image detection and tracking. Compared to traditional methods, this approach significantly improves the accuracy of recognizing human body boundaries and tracking motion. [ABSTRACT FROM AUTHOR]