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摘要: 针对现有背景建模算法难以处理场景非平稳变化的问题，提出一种基于长时间视频序列的背景建模方法.该方法包括训练、检索、更新三个主要步骤.在训练部分，首先将长时间视频分段剪辑并计算对应的背景图，然后通过图像降采样和降维找到背景描述子，并利用聚类算法对背景描述子进行分类，生成背景记忆字典.在检索部分，利用前景像素比例设计非平稳状态判断机制，如果发生非平稳变换，则计算原图描述子与背景字典中描述子之间的距离，距离最近的背景描述子对应的背景图片即为此时背景.在更新部分，利用前景像素比例设计更新判断机制，如果前景比例始终过大，则生成新背景，并更新背景字典以及背景图库.当出现非平稳变化时（如光线突变），本算法能够将背景模型恢复问题转化为背景检索问题，确保背景模型的稳定获得.将该框架与短时空域信息背景模型（以ViBe、MOG为例）融合，重点测试非平稳变化场景下的背景估计和运动目标检测结果.在多个视频序列上的测试结果表明，该框架可有效处理非平稳变化，有效改善目标检测效果，显著降低误检率.Abstract: Considering the difficulties to deal with scene non-stationary variation of proposed background modeling methods, we propose a method for moving targets by exploiting periodic spatial-temporal feature from a long-term video. We use three steps, training, retrieval and updating, to establish a background modeling framework for long-term video sequences. In the training step, we cut hours of video into a number of minute clips and compute the average background to generate a series of background images. After performing resize and dimension reduction on background images, a set of descriptors are obtained for the clustering process, where background descriptors are classified into different clusters and each cluster is represented by a typical background image in the background memory dictionary. In the retrieval step, we use foreground pixel ratio as a criterion to determine sudden change of background. For those scenarios, the current image is converted to a background descriptor and compared to the descriptors stored in retrieval database to find a suitable background frame. If no similar background descriptor is found in the database, a new background image is to be generated and added into our dictionary and background image database. Using this framework, the background modeling problem is converted to a background retrieval problem when non-stationary change happens especially for the indoor scene with quick illumination changes such as light on/off. Combining the popular ViBe or MOG algorithm with our framework, we test a number of long term video sequences and achieve better results in terms of tracking targets and the false detection rate.1) 本文责任编委 桑农
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图 10 更新背景字典阈值$T_{u}$的确定
Fig. 10 Determination of threshold $T_{u}$ for updating background dictionary
图 12 运动目标检测效果对比图(ViBe开灯)
Fig. 12 Moving object detection comparison charts (ViBe turns on the lights
图 14 前景像素比例变化对比图(对应图 12 (c) $\sim$(d))
Fig. 14 Comparison chart of foreground pixel ratio (Corresponding to Fig. 12 (c) $\sim$ (d))
图 11 运动目标检测效果对比图(ViBe关灯)
Fig. 11 Moving object detection comparison charts (ViBe turns off the lights)
图 13 前景像素比例变化对比图(对应图 11 (a) $\sim$ (b))
Fig. 13 Comparison chart of foreground pixel ratio (Corresponding to Fig. 11 (a) $\sim$ (b))
图 15 运动目标检测效果对比图(MOG关灯)
Fig. 15 Moving object detection comparison charts (MOG turns off the lights)
图 16 运动目标检测效果对比图(MOG开灯)
Fig. 16 Moving object detection comparison charts (MOG turns on the lights)
表 1 算法处理速度(fps)
Table 1 Processing times of algorithm (fps)
算法 Data1 Data2 Data3 Data4 原ViBe算法 25.96 63.79 62.44 14.49 本文算法 25.65 63.13 59.48 14.40 
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