An automated vision-based algorithm for out of context detection in images

R. Karthika, L. Parameswaran
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引用次数: 9

Abstract

Vehicular traffic on highways is a major concern relating to safety and security. Violation of traffic rules results in fatal incidents to a very large extent. In this work, an attempt has been made to detect violation of traffic rules namely vehicles in no parking and no stopping zones. Dataset consisting of cars in these zones has been used for experimentation. The proposed algorithm used histograms of oriented gradient (HOG) and Adaboost cascaded classifier for training. The traffic signs have been identified using Hough transform, Circlet transform and colour analysis. Experimental results are promising with an accuracy in the range of 90–97% with recognising no parking and no stopping sign.
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一种基于视觉的图像上下文外自动检测算法
公路上的车辆交通是与安全保障有关的一个主要问题。违反交通规则在很大程度上会导致致命事故。在这项工作中,试图检测违反交通规则的行为,即车辆在禁止停车区和禁止停车区。由这些区域中的汽车组成的数据集已用于实验。该算法使用了面向梯度直方图和Adaboost级联分类器进行训练。利用霍夫变换、Circlet变换和颜色分析对交通标志进行了识别。实验结果很有希望,识别禁止停车和禁止停车标志的准确率在90-97%之间。
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