一种视频运动目标的检测与识别方法

A Moving Object Detection and Recognition Method in Video Sequences

针对户外智能视频监视系统,研究从视频序列中自动提取和识别运动目标的方法.在运动目标检测算法中,首先引入彩色差值模型,然后进行自适应阈值分割和图像形态学后处理,并提出背景参考图像的更新方法.在人体目标识别方法中,为克服物体阴影的影响,先利用直方图技术得到检测区域中含有的运动目标数目以及每个目标的顶部位置,然后提取运动目标头肩区域的不变矩特征,并利用遗传神经网络实现运动目标的自动识别.实验表明,这是一种快速有效的多运动目标检测与识别方法.

This paper focuses on automatic extraction and recognition of moving objects in video sequences for intelligent outdoor video monitoring systems. The object extraction method is based on a color image difference model and an adaptive threshold segmentation algorithm. Moreover, an image morphology filtering method and a background image updating method are described. In order to solve the recognition problems caused by shadows of objects, the number of objects and the corresponding top position of each object in the detected region are first obtained by histogram -based technique, then the invariant moment characteristics of head-shoulder region are extracted. Finally, a GA based neural network is used to recognize the moving objects. Experimental results show that the proposed approach is effective for moving objects detection and recognition in complex outdoor background.