Multiple vehicle tracking in aerial video sequence using driver behavior analysis and improved deterministic data association

Xunxun Zhang; Hongke Xu; Jianwu Fang

doi:10.1117/1.JRS.12.016014

22 January 2018 Multiple vehicle tracking in aerial video sequence using driver behavior analysis and improved deterministic data association

Xunxun Zhang, Hongke Xu, Jianwu Fang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016014 (January 2018). https://doi.org/10.1117/1.JRS.12.016014

Abstract

Along with the rapid development of the unmanned aerial vehicle technology, multiple vehicle tracking (MVT) in aerial video sequence has received widespread interest for providing the required traffic information. Due to the camera motion and complex background, MVT in aerial video sequence poses unique challenges. We propose an efficient MVT algorithm via driver behavior-based Kalman filter (DBKF) and an improved deterministic data association (IDDA) method. First, a hierarchical image registration method is put forward to compensate the camera motion. Afterward, to improve the accuracy of the state estimation, we propose the DBKF module by incorporating the driver behavior into the Kalman filter, where artificial potential field is introduced to reflect the driver behavior. Then, to implement the data association, a local optimization method is designed instead of global optimization. By introducing the adaptive operating strategy, the proposed IDDA method can also deal with the situation in which the vehicles suddenly appear or disappear. Finally, comprehensive experiments on the DARPA VIVID data set and KIT AIS data set demonstrate that the proposed algorithm can generate satisfactory and superior results.

Citation Download Citation

Xunxun Zhang, Hongke Xu, and Jianwu Fang "Multiple vehicle tracking in aerial video sequence using driver behavior analysis and improved deterministic data association," Journal of Applied Remote Sensing 12(1), 016014 (22 January 2018). https://doi.org/10.1117/1.JRS.12.016014

Received: 11 June 2017; Accepted: 22 November 2017; Published: 22 January 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available