基于区域信息的自适应码本目标检测算法

运动物体检测是视频监控系统的一个重要组成部分。针对经典码本目标检测算法存在的自适应动态背景能力不足,以及在复杂环境下检测精确度差的问题,提出一种基于区域信息的自适应码本目标检测算法。首先,利用添加学习率的方法对背景模型进行自适应更新以使其适应不同的光照环境。其次,结合区域信息,将待检测像素的码本和周围像素的码本融合,得到更为精确的背景模型。最后,对前景像背景一样进行建模和更新,使得前景和背景模型可以在预设参数的控制下相互转化,消除由于背景变化造成的误检。实验结果表明:所提算法在有随机噪声及光照变化的复杂环境下,依然具有较好的检测率和较好的鲁棒性。基本满足动态场景中运动目标检测的精度高、速度快、抗噪强以及光照适应性好等要求。

Adaptive Codebook Detection Algorithm Based on Regional Information

Moving object detection is one of the key techniques for automatic video analysis, especially in the domain of video surveillance. To solve the problem of low ability in adaptive dynamic background and low accuracy of detection under complex environment, this paper proposes a novel adaptive codebook target detection algorithm based on regional information analysis. Firstly, it utilizes adding two learning rates in order to update adaptively the background model in the presence of background motion. Secondly, it combines the observable codebook with neighboring codebooks to obtain more accurate moving targets. Lastly, it models and updates the foreground and background respectively, and converts them to eliminate false detection caused by the partial change of background. The results of experiment show that the improved algorithm has higher recognition rate and better robustness even in complex environment conditions with illumination variation and random noise. It basically meets the demands of moving object detection in dynamic scenes in terms of precision, speed, noise resistance, light adaptability and so on.