摘要
为解决背景模型的更新问题,提高视频运动目标检测性能,通过定义像素样本对模型更新的有效因子,提出一种自适应学习的混合高斯模型检测算法。用样本有效因子的历史累加量反映背景模型的质量,并用于动态调整模型更新速度。同时,对检测出的前景区域进行目标分析,由分析结果间接控制模型更新,保证更新的准确性和模型的稳定性。实验结果表明,该算法可以快速适应背景变化,同时保证目标检测的完整性。算法性能已在不同监控场景中得到验证。
Background subtraction is a widely used method for video object detection and its performance is dependent on the quality of background model. In this paper, an algorithm for video target detection based on adaptive learning GMM was proposed by defining an efficiency factor between pixel samples and their background models. The accumulation of efficiency factor（AEF） shows how well the models can represent the background and was used to adjust the learning-rate dynamically. At the same t^me, how to update the models was dependent on the changes of the background after the foreground image analysis. The performance and robustness of the algorithm has been verified experimentally.
出处
《中国图象图形学报》
CSCD
北大核心
2010年第4期631-636,共6页
Journal of Image and Graphics
关键词
混合高斯模型
智能视频监控
自适应学习
Gaussian mixture models（GMM） , intelligence video surveillance, adaptive learning
