A novel adaptive motion detection based on k-means clustering
Detecting a high-quality moving object with good robustness in computer vision system has important significance for follow-up task. Researching on the traditional algorithm, this paper proposes a background reconstruction algorithm based on a modified k-means clustering and the Single Gaussian model which could provide an accurate background image through a sequence of scene images with foreground objects. Then based on the statistical characteristics of the background pixels region detects the moving object. Aiming to the effect of dynamic changes of the environment, this paper proposes a method of robust adaptive motion detection Combined with the principle of Mathematical Morphology and Region-labeling. Experiments prove this method can complete the task of moving object detection in complex environment.