Institute of Geospatial Information, Information Engineering University, 62 Science Avenue, Zhengzhou 450001, China.
Sensors (Basel). 2023 Jun 20;23(12):5771. doi: 10.3390/s23125771.
The complex backgrounds of satellite videos and serious interference from noise and pseudo-motion targets make it difficult to detect and track moving vehicles. Recently, researchers have proposed road-based constraints to remove background interference and achieve highly accurate detection and tracking. However, existing methods for constructing road constraints suffer from poor stability, low arithmetic performance, leakage, and error detection. In response, this study proposes a method for detecting and tracking moving vehicles in satellite videos based on the constraints from spatiotemporal characteristics (DTSTC), fusing road masks from the spatial domain with motion heat maps from the temporal domain. The detection precision is enhanced by increasing the contrast in the constrained area to accurately detect moving vehicles. Vehicle tracking is achieved by completing an inter-frame vehicle association using position and historical movement information. The method was tested at various stages, and the results show that the proposed method outperformed the traditional method in constructing constraints, correct detection rate, false detection rate, and missed detection rate. The tracking phase performed well in identity retention capability and tracking accuracy. Therefore, DTSTC is robust for detecting moving vehicles in satellite videos.
卫星视频的复杂背景以及噪声和伪运动目标的严重干扰使得检测和跟踪移动车辆变得困难。最近，研究人员提出了基于道路的约束条件来去除背景干扰，以实现高精度的检测和跟踪。然而，现有的道路约束构建方法存在稳定性差、算法性能低、泄漏和错误检测等问题。针对这些问题，本研究提出了一种基于时空特征的卫星视频中移动车辆检测与跟踪方法（DTSTC），融合了空间域的道路掩模和时域的运动热力图。通过增加约束区域的对比度来提高检测精度，从而准确检测移动车辆。通过利用位置和历史运动信息完成帧间车辆关联来实现车辆跟踪。该方法在不同阶段进行了测试，结果表明，与传统方法相比，该方法在构建约束条件、正确检测率、错误检测率和漏检率方面具有更好的性能。在身份保留能力和跟踪精度方面，跟踪阶段表现良好。因此，DTSTC 是一种用于卫星视频中移动车辆检测的稳健方法。
