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Background subtraction and human detection in outdoor videos using fuzzy logic

机译:使用模糊逻辑对户外视频进行背景扣除和人体检测

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This paper presents a scale and view invariant approach for human detection in the presence of various other objects like animals, vehicles, etc. Human detection is one of the essential steps in applications like activity recognition, gait recognition, human centric surveillance etc. Inaccurate detection of humans in such applications may increase the number of false alarms. In the proposed work, fuzzy logic has been used to model a robust background for object detection. Three different features are extracted from the contours of the detected objects. These features are aggregated using fuzzy inference system. Then human contour is identified using template matching. The proposed method consists of four main steps; Moving Object Detection, Feature Extraction, Feature Aggregation, and Human Contour Detection.
机译:本文提出了在存在其他各种物体(例如动物,车辆等)的情况下进行人体检测的比例和视图不变方法。人体检测是诸如活动识别,步态识别,以人为中心的监视等应用程序中的必要步骤之一。不准确的检测在此类应用中,大量的人可能会增加错误警报的数量。在拟议的工作中,模糊逻辑已用于为对象检测建立鲁棒的背景模型。从检测到的物体的轮廓中提取三个不同的特征。这些特征使用模糊推理系统进行汇总。然后使用模板匹配来识别人的轮廓。所提出的方法包括四个主要步骤。运动对象检测,特征提取,特征聚合和人体轮廓检测。

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