Research on Vehicle Detection and Tracking Algorithm Based on the Methods of Frame Difference and Adaptive Background Subtraction Difference

DOI

10.2991/aiie-16.2016.32How to use a DOI?

Keywords

target detection; frame difference method; background subtraction difference method; harris corner detection; clustering analysis

Abstract

This paper proposed methods of vehicle detection and tracking algorithm in real-time traffic. In the detection of real-time moving vehicle, vehicle areas would be determined through road line detection. Then, the main color information of moving and non-moving area would be obtained through frame difference. Filling the main color information in vehicle moving area would lead to a similar background image. At last, moving vehicles would be determined through adaptive Background Subtraction difference. In the tracking of moving vehicles, firstly, all characteristic corners can be got by using Harris detection. Then, all characteristic corner set in the separate moving area would be collected through cluster analysis. For each characteristic individual corner set can generate a circle embracing all characteristics, some problems like vehicle barrier could be analyzed by using the radius of characteristic circle. At last, conduct feature matching tracking by using the center of feature circle. Experimental result shows that the improving algorithm can extract all moving objects, which was endowed with strong background adaptability and better real-time performance.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Yiqin Cao
AU  - Xiao Yun
AU  - Tao Zhong
AU  - Xiaosheng Huang
PY  - 2016/11
DA  - 2016/11
TI  - Research on Vehicle Detection and Tracking Algorithm Based on the Methods of Frame Difference and Adaptive Background Subtraction Difference
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 134
EP  - 140
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.32
DO  - 10.2991/aiie-16.2016.32
ID  - Cao2016/11
ER  -