自动驾驶汽车系统中基于区域和直方图特征的路灯目标识别

Martins E. Irhebhude, M. Shabi, A. Kolawole
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引用次数: 2

摘要

本文使用图像处理的概念来解决路灯目标识别问题。提出了一种基于图像处理技术的图像特征选择与处理方法。对提取的图像进行直方图和区域处理。然后提取直方图和区域特征,并利用其训练支持向量机(SVM)分类器进行路灯识别。实验结果表明,直方图、区域特征和两者结合的准确率分别为99.1%、84%和100%。实验结果表明,该方法具有较好的鲁棒性、准确性和较强的目标识别能力。
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STREETLIGHT OBJECTS RECOGNITION BY REGION AND HISTOGRAM FEATURES IN AN AUTONOMOUS VEHICLE SYSTEM
In this paper Streetlight object identification is addressed using the notion of image processing. An approach based on Image Processing Techniques is proposed for selection and processing of features from the images. Histogram and Region was applied on the extracted images. Histogram and Region features were then extracted and employed to train the Support Vector Machine (SVM) classifier for streetlight recognition. Experimental results shows 99.1%, 84% and 100% for histogram, region features and combination of both respectively. Experimental results have proved that the proposed method is robust, accurate, and powerful in object recognition.
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DIMENSIONALITY REDUCTION BASED CLASSIFICATION USING GENERATIVE ADVERSARIAL NETWORKS DATASET GENERATION ADVANCED COLOR COVERT IMAGE SHARING USING ARNOLD CAT MAP AND VISUAL CRYPTOGRAPHY STREETLIGHT OBJECTS RECOGNITION BY REGION AND HISTOGRAM FEATURES IN AN AUTONOMOUS VEHICLE SYSTEM SMART GESTURE USING REAL TIME OBJECT TRACKING CLASSIFICATION OF BRAIN TUMOR USING BEES SWARM OPTIMISATION
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