22 January 2018 Multiple vehicle tracking in aerial video sequence using driver behavior analysis and improved deterministic data association
Author Affiliations +
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.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Xunxun Zhang, Xunxun Zhang, Hongke Xu, Hongke Xu, Jianwu Fang, 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 . Submission: Received: 11 June 2017; Accepted: 22 November 2017
Received: 11 June 2017; Accepted: 22 November 2017; Published: 22 January 2018
JOURNAL ARTICLE
23 PAGES


SHARE
Back to Top