Automated Counting and Tracking of Vehicles

Frank Y. Shih, Xin Zhong

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

A robust traffic surveillance system is crucial in improving the control and management of traffic systems. Vehicle flow processing primarily involves counting and tracking vehicles; however, due to complex situations such as brightness changes and vehicle partial occlusions, traditional image segmentation methods are unable to segment and count vehicles correctly. This paper presents a novel framework for vision-based vehicle counting and tracking, which consists of four main procedures: foreground detection, feature extraction, feature analysis, and vehicles counting/tracking. Foreground detection intends to generate regions of interest in an image, which are used to produce significant feature points. Vehicles counting and tracking are achieved by analyzing clusters of feature points. As for testing on recorded traffic videos, the proposed framework is verified to be able to separate occluded vehicles and count the number of vehicles accurately and efficiently. By comparing with other methods, we observe that the proposed framework achieves the highest occlusion segment rate and the counting accuracy.

Original languageEnglish (US)
Article number1750038
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume31
Issue number12
DOIs
StatePublished - Dec 1 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Vehicle counting
  • clustering
  • feature extraction
  • foreground detection
  • vehicle tracking

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