An adaptive Kalman predictor applied to tracking vehicles in the traffic monitoring system

Author

Qiu, Zhijun ; An, Dexi ; Yao, Danya ; Zhou, Donghua ; Ran, Bin

fYear

2005

fDate

6-8 June 2005

Firstpage

230

Lastpage

235

Abstract

Video object tracking is an important method of traffic data collection in ITS. This paper implements an approach for detecting traffic objects in urban traffic scenes by means of feature-based reasoning on visual data, and tries to track and classify traffic objects with self-defined features. An adaptive Kalman prediction algorithm is presented to improve the prediction accuracy of location. Experimental results are also shown to demonstrate the effectiveness of the proposed algorithm.

Keywords

adaptive Kalman filters; automated highways; computerised monitoring; feature extraction; image classification; image matching; inference mechanisms; object detection; road traffic; tracking; traffic engineering computing; video signal processing; ITS; adaptive Kalman prediction algorithm; feature-based reasoning; self-defined feature; traffic data collection; traffic monitoring system; traffic object classification; traffic object detection; urban traffic scene; vehicle tracking; video object tracking; visual data; Accuracy; Feature extraction; Kalman filters; Layout; Monitoring; Object detection; Prediction algorithms; Real time systems; Traffic control; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Vehicles Symposium, 2005. Proceedings. IEEE

Print_ISBN

0-7803-8961-1

Type

conf

DOI

10.1109/IVS.2005.1505107

Filename

1505107