关键词: 视频图像分割, 贝叶斯学习, 复杂背景模型

Abstract:

Segmentation is the first step towards the target location and recognition in video sequences. This task becomes difficult when the background illumination changes. A Bayesian learning method was applied into video segmentation. The constantly changing background was modeled at the pixel level. The histogram colors and co-occurrence vectors were calculated. The feature vector for each pixel was represented with a discrete probability distribution function. A Bayesian learning method was used to obtain these probability distribution functions from the video image inputs. Experiment results indicate that the proposed approach is able to learn a complex background of which the illumination changes gradually or suddenly.

Key words: video image segmentation, Bayesian learning, complex background modeling