Vehicle detection and recognition for intelligent traffic survelliance system 论文

本文探讨了智能交通监控系统中车辆检测与识别的过程,主要涉及Haar分类器的使用,包括Haar-like特征、积分图法、AdaBoost算法以及级联分类器。Haar特征用于车辆检测,结合积分图法能快速计算特征值,AdaBoost算法通过级联多个弱分类器形成强分类器,提高检测准确性。

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Vehicle detection and recognition for intelligent traffic survelliance system

一、过程梳理

  • a)Vehicle detection(车辆检测)
    • 1、Haar-like feature
    • 2、AdaBoost algorithm
  • b)Vehicle recognition(车辆识别)
    • 1、Garbor wavelets transform
    • 2、Local gabor binary pattern and histogram sequence (LGBPHS)
  • 3、feature dimension reduction(using PCA)

二、Haar分类器

A)介绍

Haar分类器 = Haar-like特征 + 积分图方法 + AdaBoost +级联;

B)Haar分类器算法的要点如下:
  • ① 使用Haar-like特征做检测。
  • ② 使用积分图(Integral Image)对Haar-like特征求值进行加速。
  • ③ 使用AdaBoost算法训练区分人脸和非人脸的强分类器。
  • ④ 使用筛选式级联把强分类器级联到一起,提高准确率。

三、Haar特征(Haar-like feature)

Haar特征起源于人脸识别
资料站:

With the rapid development of China's economy, the per capita share of cars has rapidly increased, bringing great convenience to people's lives. However, with it came a huge number of traffic accidents. A statistical data from Europe shows that if a warning can be issued to drivers 0.5 seconds before an accident occurs, 70% of traffic accidents can be avoided. Therefore, it is particularly important to promptly remind drivers of potential dangers to prevent traffic accidents from occurring. The purpose of this question is to construct a machine vision based driving assistance system based on machine vision, providing driving assistance for drivers during daytime driving. The main function of the system is to achieve visual recognition of pedestrians and traffic signs, estimate the distance from the vehicle in front, and issue a warning to the driver when needed. This driving assistance system can effectively reduce the probability of traffic accidents and ensure the safety of drivers' lives and property. The main research content of this article includes the following aspects: 1. Implement object detection based on the YOLOv5 model. Conduct research on convolutional neural networks and YOLOv5 algorithm, and develop an object detection algorithm based on YOLO5. Detect the algorithm through road images, and analyze the target detection algorithm based on the data returned after training. 2. Estimate the distance from the front vehicle based on a monocular camera. Study the principle of estimating distance with a monocular camera, combined with parameters fed back by object detection algorithms, to achieve distance estimation for vehicles ahead. Finally, the distance estimation function was tested and the error in the system's distance estimation was analyzed. 3. Design and implementation of a driving assistance system. Based on the results of two parts: target detection and distance estimation, an intelligent driving assistance system is constructed. The system is tested through actual road images, and the operational effectiveness of the intelligent driving assistance system is analyzed. Finally, the driving assistance system is analyzed and summarized.
06-03
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