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

机译:一种高效灵活的空中视频序列中多车辆跟踪方法

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摘要

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.
机译:空中视频序列中的多车跟踪(MVT)可以为应用提供有用的信息,例如交通流分析。由于对跟踪效率和可变数量的车辆有很高的要求,因此具有挑战性。此外,如果车辆被树木,建筑物和大型车辆的阴影遮挡,则尤其具有挑战性。在本文中,提出了一种有效且灵活的航空视频序列中的MVT方法。首先,作为解决MVT问题的第一步,通过将两帧差异和超像素分割相结合,实现了基于超像素分割的多车辆检测(MVD)。两帧差用于减少搜索空间。通过经由超像素的中心扫描搜索空间,可以有效地检测出行驶中的车辆。然后,提出了确定性数据关联来解决MVT问题。为了提高跟踪精度,我们融合了多种功能以建立成本函数。对于车辆的可变数量,通过建立或删除跟踪器来设计跟踪器管理。此外,对于遮挡处理,我们专注于准确的状态估计,并且它是通过基于驾驶员行为的卡尔曼滤波器(DBKF)方法实现的。在DBKF方法中,我们认真考虑驾驶员的行为,包括速度限制和避免与前车的追尾碰撞。跟踪器管理和遮挡处理都使MVT方法可以灵活地应对各种交通场景。最后,对DARPA VIVID数据集和KIT AIS数据集的综合实验表明,所提出的MVT算法可以产生令人满意的优异结果。

著录项

  • 来源
    《International journal of remote sensing》 |2018年第20期|6234-6265|共32页
  • 作者

    Zhang Xun Xun; Xu Hong Ke;

  • 作者单位

    Changan Univ, Sch Elect & Control Engn, Middle Sect Nan Erhuan Rd, Xian 710064, Shaanxi, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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