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

机译:基于自动视觉的基于视觉算法,用于在图像中的上下文检测

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摘要

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.
机译:高速公路上的车辆交通是与安全和安全有关的主要关注点。 违反交通规则在很大程度上导致致命事件。 在这项工作中,已经尝试发现违反交通规则即没有停车的车辆,也没有停车区。 由这些区域中的汽车组成的数据集已用于实验。 所提出的算法使用了面向梯度(HOG)和Adaboost级联分类器的直方图进行训练。 已经使用Hough变换,Circlet变换和颜色分析识别了交通标志。 实验结果是有前途的,精度在90-97%的范围内,识别不禁止停车,没有停车标志。

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