基于局部更新的分层码本目标检测算法

为了解决复杂环境下如树木摇摆、水波晃动等波动式干扰及光照变化对运动目标检测产生影响的问题，给出了一种基于码本模型的运动目标检测算法。考虑到实际场景中背景的变化主要体现在亮度方面，首先对视频序列图像进行颜色空间转化，由RGB空间转化到YUV空间，然后利用Box模型优化了码本模型参数和训练策略。目标检测时，采用局部背景更新方法，即利用帧差法确定变化区域，结合分层码本思想，实时更新背景模型，以达到精确提取运动目标的目的。对比实验表明在背景中存在扰动或者光照发生变化等情况下，该算法都能够对运动目标进行有效检测，具有一定实用性和鲁棒性。

Moving object detection based on local updated layered codebook

In background subtraction, it is challenging to detect foreground objects in the presence of complex background motions including waving trees, rippling water, illumination changes, etc. In order to solve this problem, a codebook-based object detection algorithm is proposed in the paper. Given that in actual scene the change of background reflects on brightness, color space is transformed from RGB space to YUV space for video sequences. Then the algorithm establishes a Box model which makes the codewords representation and training period more compact than the standard codebook. Besides, A local updated method, namely through frame difference to detect the region of variation, is incorporated into layered codebook to update the background real-timely, thus achieving more accurate foreground detection. Comparative results indicate that the algorithm can handle scenes containing moving backgrounds or illumination variations, and it achieves robust object detection for different types of video.