利用时空背景模型的快速运动目标检测方法

为了弥补运动目标检测中传统混合高斯背景模型仅对单个像素建模、运算耗时的不足，通过提取背景时间统计特征和空间区域特征建立模型，针对模型中的高斯分量采用一种改进的分量个数自适应算法，并在此模型基础上，提出一种自适应迭代分块目标检测方法。通过包含区域信息的背景模型检测目标，减少在同一背景区域中目标的误判和漏判。将自适应迭代分块检测算法与背景的区域信息结合，可以在不降低检测精度的前提下大大提高算法执行速度。实验结果表明，相对于传统算法，本文检测法检测结果信噪比更高，目标更加完整，运行速度平均提高了22%。

Fast moving object detection method using temporal-spatial background model

Moving objects extraction is a key part of video surveillance system. To improve the performance of moving objects detection method based on the Gaussian Mixture Model(GMM), an iterative detection algorithm with adaptive partitioning block of pixels is proposed. It is based on the temporal-spatial background that the number of components is improved adaptively and the feature of areas extracted spatially is combined. With the spatial areas information, the algorithm decreases the number of small fake objects and reduces the fragmentation of objects that caused by all kinds of noise. Comparing with detection method based on single pixel, the proposed method would not almost impact the detected results when it reduces the algorithm computation obviously. The results show that the objects extracted by the proposed method with higher SNR and the processing time decreases 22% contrasting to traditional algorithm.