多车辆识别采用混合斑点分析和基于特征的方法

Anchisa Chantakamo, M. Ketcham
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引用次数: 21

摘要

本文提出了一种基于视频数据的车辆颜色检测和多车分类方法。采用斑点分析和基于特征的方法对多车辆进行分类。该方法使用交通监控摄像机记录的视频文件作为输入。该技术采用RGB(红、绿、蓝)对车辆图像进行颜色检测。利用光流将车辆与背景分离。对车辆类型进行Blob分析和特征分析。基于特征的提取是通过局部颜色聚类实现的。该方法可以将车辆图像分为轿车、皮卡和卡车三种类型。使用k近邻算法来检测可能的颜色。所提出的车辆识别方法可用于识别与可疑车辆输入(类型和颜色)相匹配的目标车辆,并提供实时交通监控信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The multi vehicle recognition using hybrid blob analysis and feature-based
This paper presents an approach method to detect vehicle color and to classify multi vehicle from video data. The multi vehicle is classified by blob analysis and feature-based. The proposed method uses a video file recorded by traffic surveillance camera as input. This technique applied RGB (Red, Green and Blue) to detect color of vehicle image. The vehicle is separated from background by using optical flow. Blob analysis and feature-based are performed the type of vehicles. Feature-based is extracted by localized color clusters. This method can classify vehicle images into 3 types of car, pickup, and truck. The K-Nearest Neighbor algorithm is used to detect color possible. The proposed vehicle recognition method can be applied to spot target vehicles which match the suspect car input (type and color) and provide real-time and traffic surveillance information.
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