Abstract:

The prior work of video analysis technology is video foreground detection in complex scenes. In order to solve the problem of low accuracy in foreground moving target detection, an improved moving object extraction algorithm for video based on Visual Background Extractor (ViBE), called ViBE⁺, was proposed. Firstly, in the model initialization stage, each background pixel was modeled by a collection of its diamond neighborhood to simply the sample information. Secondly, in the moving object extraction stage, the segmentation threshold was adaptively obtained to extract moving object in dynamic scenes. Finally, for the sudden illumination change, a method of background rebuilding and update-parameter adjusting was proposed during the process of background update. The experimental results show that, compared with the Gaussian Mixture Model (GMM) algorithm, Codebook algorithm and original ViBE algorithm, the improved algorithm's similarity metric on moving object extracting results increases by 1.3 times, 1.9 times and 3.8 times respectively in complex video scene LightSwitch. The proposed algorithm has a better adaptability to complex scenes and performance compared to other algorithms.

Key words: foreground extraction, Visual Background Extractor (ViBE), background modeling, self-adaptive threshold, update-parameter

摘要：

在复杂场景下的视频运动目标提取是视频分析技术的首要工作。为了解决前景运动目标提取的精确度不高的问题,提出一种基于视觉背景提取(ViBE)的改进视频运动目标提取算法(ViBE⁺)。首先,在背景模型初始化阶段采用像素的菱形邻域来简化样本信息;其次,在前景运动目标提取阶段引入自适应分割阈值来适应场景的动态变化;最后,在更新阶段提出背景重建和调整更新因子方法来处理光照变化的情形。实验结果表明,对于复杂视频场景LightSwitch的运动目标提取结果在相似度指标上,改进后的算法与混合高斯模型(GMM)算法、码本模型算法以及原始ViBE算法相比,分别提高了1.3倍、1.9倍以及3.8倍。所提算法能够在有效时间内对复杂场景具有较好的自适应性,且性能明显优于对比算法。

关键词: 前景提取, 视觉背景提取, 背景建模, 自适应阈值, 更新因子