图像挖掘在交通图像分析知识发现中的应用

S. Zaboli, S. Naderi, A. Moghaddam
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引用次数: 3

摘要

本文提出了图像挖掘系统在交通/人员行为知识发现中的一种新应用。我们提出的图像挖掘框架在交通监控领域通过背景减除、图像分割和目标跟踪对交通图像序列进行分析。在每一帧中发现并准确捕获和建模车辆对象的时空关系。然后确定运动物体的运动轨迹,识别人/车行为的正常与异常。该系统采用模糊决策模型(FDM)对交通行为模式进行建模和知识提取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Image Mining for Knowledge Discovery of Analyzed Traffic Images
This paper presents a new application of image mining systems for knowledge discovery of traffic/people behaviors. Our proposed image-mining framework analyzes the traffic image sequences by using background subtraction, image segmentation, and object tracking in the domain of traffic monitoring. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Then trajectory of moving objects is determined and normality and abnormality of people/vehicle behaviors is recognized. In our system a fuzzy Decision Maker (FDM) is used to modeling and extracting knowledge from traffic behavioral patterns.
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