PubAg

Main content area

An efficient and flexible approach for multiple vehicle tracking in the aerial video sequence

Author:
Zhang, Xun Xun, Xu, Hong Ke
Source:
International journal of remote sensing 2018 v.39 no.19 pp. 6234-6265
ISSN:
1366-5901
Subject:
algorithms, buildings, data collection, remote sensing, traffic, trees
Abstract:
Multiple vehicle tracking (MVT) in the aerial video sequence can provide useful information for the applications such as traffic flow analysis. It is challenging due to the high requirement for the tracking efficiency and variable number of the vehicles. Furthermore, it is particularly challenging if the vehicles are occluded by the shadow of the trees, buildings, and large vehicles. In this article, an efficient and flexible MVT approach in the aerial video sequence is put forward. First, as the pre-step to approach the MVT problem, the superpixel segmentation-based multiple vehicle detection (MVD) is achieved by combining the two-frame difference and superpixel segmentation. The two-frame difference is utilized to reduce the search space. By scanning the search space via the centre of the superpixels, the moving vehicles are detected efficiently. Then, the deterministic data association is proposed to tackle the MVT problem. To improve the tracking accuracy, we fuse multiple types of features to establish the cost function. With respect to the variable number of the vehicles, the tracker management is designed by establishing or deleting the trackers. Furthermore, for the occlusion handling, we focus on the accurate state estimation, and it is realized by the driver behaviour-based Kalman filter (DBKF) method. In the DBKF method, we take seriously into account the driver behaviour, including the speed limit and rear-end collision avoidance with the front vehicle. Both tracker management and occlusion handling make the MVT approach flexibly cope with varieties of traffic scenes. Finally, comprehensive experiments on the DARPA VIVID data set and KIT AIS data set demonstrate that the proposed MVT algorithm can generate satisfactory and superior results.
Agid:
6198042