An Implementation of Moving Object Detection, Tracking and Counting Objects for Traffic Surveillance System

2011 International Conference on Computational Intelligence and Communication Networks Pub Date : 2011-10-07 DOI:10.1109/CICN.2011.28

Yoginee B. Brahme, P. S. Kulkarni

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引用次数: 23

Abstract

Moving vehicle detection in digital image sequences is one of the key technologies of Intelligent Transportation Systems (ITS). Traffic Surveillance System is being more and important with the enlarging of urban scale and increasing number of vehicles. This Paper presents an intelligent vehicle counting method based on blob analysis in traffic surveillance. The algorithm is composed of moving object segmentation, blob analysis, and tracking. By analyzing the blob of vehicles, the meaningful features are extracted. In addition, the speed of each vehicle and the vehicle flow through a predefined area can be calculated by analyzing blobs of vehicles. The experimental results show that the proposed system can provide useful information for traffic surveillance. We analyze the procedure of video-based traffic congestion system and divide it into graying, binarization, denoising and moving target detection. The system first reads video and converts them into grayscale images. We also put forward a Boundary block detection algorithm with noise reduction to identify the moving objects.

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交通监控系统中运动目标检测、跟踪和计数的实现

数字图像序列中的运动车辆检测是智能交通系统的关键技术之一。随着城市规模的扩大和车辆数量的增加，交通监控系统显得越来越重要。提出了一种基于blob分析的交通监控智能车辆计数方法。该算法由运动目标分割、斑点分析和跟踪组成。通过对车辆斑点的分析，提取有意义的特征。此外，可以通过分析车辆斑点来计算每辆车的速度和通过预定义区域的车辆流量。实验结果表明，该系统可以为交通监控提供有用的信息。分析了基于视频的交通拥堵系统的处理过程，将其分为灰度化、二值化、去噪和运动目标检测。该系统首先读取视频并将其转换为灰度图像。提出了一种带降噪的边界块检测算法来识别运动目标。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2011 International Conference on Computational Intelligence and Communication Networks

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