【摘要】 基于视频的运动分析是近年来计算机视觉领域备受关注的研究方向。提出了一种用于视频监控系统的运动目标分类算法,可以将运动目标分类为几种事先定义好的类别:人、人群、汽车、自行车。定义了运动目标的几种简单有效的形状特征,并选择基于小样本学习理论的支持向量机对不同目标进行分类。最后,提出隔帧分类的思想并描述了几种提高分类效率的方法,以满足实时性要求。实验证明该方法能较好地区分人、人群、车、自行车等目标。更多还原

【Abstract】 Video-based motion analysis is receiving increasing attention in the domain of computer vision.An object classification algorithm is proposed in this paper used for video surveillance,which can classify moving objects into predefined four categories: human being,crowd,car and bicyclist.In this paper,several simple shape features of moving objects are defined and the SVM(Support Vector Machines),which based on small samples statistical learning theory,is chosen to classify different objects.At last,to meet the real-time requirement,the method of alternative classification is presented and several other methods that can improve the efficiency of classification are described.Experiments show that the method can accurately distinguish human being,crowd,car,and bicyclist.更多还原