针对非参数核密度估计在前期学习阶段信息冗余和计算量大,在后期背景更新阶段自适应性差需手动调整阈值和检测结果出现阴影等问题,提出一种基于局部时空域模型的核密度估计目标检测方法。在前期训练学习阶段采用K均值聚类选择关键帧,从而避免信息冗余和计算量大问题;在后期背景更新阶段,构建一种局部时空域模型,在时间域通过历史帧信息自适应调整时间域窗口大小,在空间域利用颜色和LBP描述的纹理特征消除部分阴影问题。在复杂场景下的实验结果表明,该算法在实时性和检测准确率方面有效得到提高。

In this paper, we propose a new method for foreground object detection based on the Kernel: Density Estimation of a local spatio-temporal model (LST-KDE), which overcomes information redundancy and the large calculated quantity problem in the training phase as well as the manual adjusting time window size and shadow problem in the detection and updating background phase. The LST-KDE algorithm uses the k-means clustering algorithm to optimize the sample set and to choose the key frames in the training phase. Therefore, it can avoid information redundancy and the large calculated quantity problem. In the detection and updating background phase, the LST-KDE algorithm constructs a local spatio-temporal model. This method can not only adaptively set the time window size by using history frame information in a temporal model, but also uses color and texture features described with the local binary pattern (LBP) algorithm to remove shadows in the spatial model. The experiment in a complex environment demonstrates that the proposed method outperforms recent state-of-the-art methods.