Spatial Feature Based Shadow Detection in Visual Traffic Surveillance System

Shaohua Xu, Yong Zhao, Chunyu Yu, Ling Shen
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引用次数: 1

Abstract

A shadow detection algorithm base on spatial features was proposed, in the case of focusing on traffic vehicle detection system. First of all, multi-foreground rectangles were extracted by using Gaussian mixture model (GMM) and edge detection operator of mathematical morphology. Then, histogram of horizontal location - foreground point number of vertical direction was computed, combined with optimum threshold segmentation, shadow areas were removed. To enhance the adaptability, the system learns direction relations between target and its shadow, automatically. Results were presented for several video sequences representing a variety of illumination conditions. Experimental results in different traffic conditions showed our technique robust, self-adaptive, and real-time.
基于空间特征的视觉交通监控系统阴影检测
针对交通车辆检测系统,提出了一种基于空间特征的阴影检测算法。首先,利用高斯混合模型(GMM)和数学形态学边缘检测算子提取多个前景矩形;然后,计算水平位置-垂直方向前景点个数直方图,结合最优阈值分割,去除阴影区域;为了增强自适应能力,系统自动学习目标与阴影之间的方向关系。给出了代表各种光照条件的几个视频序列的结果。在不同交通条件下的实验结果表明,该技术具有鲁棒性、自适应性和实时性。
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