Name
Title | ||
|---|---|---|
Automatic Measurement Of Traffic State Parameters Based On Computer Vision For Intelligent Transportation Surveillance |
Abstract | ||
|---|---|---|
Online automatic measurement of traffic state parameters has important significance for intelligent transportation surveillance. The video-based monitoring technology is widely studied today but the existing methods are not satisfactory at processing speed or accuracy, especially for traffic scenes with traffic congestion or complex road environments. Based on technologies of computer vision and pattern recognition, this paper proposes a novel measurement method that can detect multiple parameters of traffic flow and identify vehicle types from video sequence rapidly and accurately by combining feature points detection with foreground temporal-spatial image (FTSI) analysis. In this method, two virtual detection lines (VDLs) are first set in frame images. During working, vehicular feature points are extracted via the upstream-VDL and grouped in unit of vehicle based on their movement differences. Then, FTSI is accumulated from video frames via the downstream- VDL, and adhesive blobs of occlusion vehicles in FTSI are separated effectively based on feature point groups and projection histogram of blob pixels. At regular intervals, traffic parameters are calculated via statistical analysis of blobs and vehicles are classified via a K-nearest neighbor (KNN) classifier based on geometrical characteristics of their blobs. For vehicle classification, the distorted blobs of temporary stopped vehicles are corrected accurately based on the vehicular instantaneous speed on the downstream-VDL. Experiments show that the proposed method is efficient and practicable. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S0218001418550030 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Computer vision, intelligent transportation monitoring, measurement of traffic parameters, classification of vehicles, K-nearest neighbor classifier, foreground temporal-spatial image | Computer vision,Traffic flow,Advanced Traffic Management System,Artificial intelligence,Intelligent transportation system,Mathematics,Traffic congestion | Journal |
Volume | Issue | ISSN |
32 | 4 | 0218-0014 |
Citations | PageRank | References |
3 | 0.39 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqiang Ren | 1 | 49 | 9.27 |
| Chunhong Zhang | 2 | 93 | 20.35 |
| Lingjuan Zhang | 3 | 3 | 0.39 |
| Ning Wang | 4 | 3 | 0.39 |
| Yue Feng | 5 | 55 | 16.15 |