摘要
在对轨迹流矢量进行量化编码的基础上,提出了一种基于深度优先搜索的轨迹分布模式提取算法,生成了能够描述轨迹分布的序列模式图,并给出了与之相应的异常检测方法。对不同场景的序列图像的实验表明,该方法不仅能够学习轨迹中流矢量的分布,而且能够反映它们之间的时序关系,可以应用于室外复杂场景的目标异常行为检测。
This paper puts forward a novel depth first search method to learning the distribution of motion trajectory based on the vector quantization of the flow vectors. Then the sequential patterns graph is generated, which visually represents the trajectory pattern. The corresponding anomaly detection method is also given in this paper. The experiments on different sites demonstrate that our method can not only discover the flow vector's distribution but also can reflect their time orders effectively, which makes it suitable for anomaly detection in outdoor scenes.
出处
《计算机应用》
CSCD
北大核心
2007年第5期1126-1128,1131,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(60472072)
航空科学基金资助项目(04I50370)
陕西理工学院基础研究项目(SLG0631)
关键词
轨迹分析与学习
矢量量化
深度优先搜索
trajectory analysis and learning
vector quantization
depth first search