Co-training algorithm based on on-line boosting for vehicle tracking

Author

Wen-hui Li ; Pei-xun Liu ; Ying Wang ; Yu-chao Zhou ; Lei Wang ; Chao Wen ; Hong-yin Ni ; Qian-li Xing

Author_Institution

State Key Lab. of Automotive Simulation & Control, Jilin Univ., Changchun, China

fYear

2013

fDate

26-28 Aug. 2013

Firstpage

592

Lastpage

596

Abstract

The current vehicle tracking algorithms cannot meet the requirements of high robustness in engineering application. A co-training algorithm based on on-line boosting for vehicle tracking is proposed. In this algorithm, first the vehicle region of interest is detected by vehicle-shadow feature and vehicle horizontal edge feature. Then the vehicle region of interest is verified by off-line classifiers which are learned from Haar feature and Adaboost algorithm. Finally, a co-training algorithm based on on-line boosting is used for further vehicle tracking, then the tracking window was reshaped according to the shadow of target vehicle. Experiments show that the proposed algorithm has high robustness and flexibility with good application prospects.

Keywords

edge detection; feature extraction; image classification; learning (artificial intelligence); object tracking; road vehicles; traffic engineering computing; Adaboost algorithm; Haar feature; application prospects; cotraining algorithm; interest region; offline classifiers; online boosting; vehicle horizontal edge feature; vehicle tracking; vehicle-shadow feature; Boosting; Classification algorithms; Image edge detection; Robustness; Target tracking; Vehicles; intelligent vehicles; on-line boosting; vehicle detection; vehicle tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation (ICIA), 2013 IEEE International Conference on

Conference_Location

Yinchuan

Type

conf

DOI

10.1109/ICInfA.2013.6720366

Filename

6720366