ABSTRACT

Ｗith the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information this paper, a tiny vehicle detection method based on spatio-temporal information is proposed to constrain the significance of the image. Firstly, the background modelling method is used to obtain the motion heat map of the image and constrain the motion region. A significance detection method for small targets was used to obtain the significance mapping of these regions. Finally, the detection results were optimized by combining the significance neighbourhood information and the time information between frames to output the binary target detection map. Finally, taking different urban road scenes in ‘Jilin-1’satellite video as examples and compares a variety of existing algorithms. Experiments prove that the proposed algorithm can maintain false alarm rate of less than 10% when the detection accuracy and recall rate reach 85% and has certain anti-interference ability in the image environment with satellite Angle deviation.

摘要

随着遥感技术的迅猛发展，卫星视频已成为车辆检测的重要数据来源，为监控提供了广阔的领域。所完成的工作通常集中于基于特征提取的具​​有中等大小对象的航拍视频。但是，卫星视频图像中行驶的车辆的范围从几个像素到几十个像素，并且相对于背景显示出较低的对比度，这使得难以获得可用的外观或形状信息。时空信息被提出来约束图像的重要性。首先，使用背景建模方法获得图像的运动热图并约束运动区域。使用小目标的显着性检测方法来获得这些区域的显着性图。最后，结合有效邻域信息和帧间时间信息，对检测结果进行优化，输出二值目标检测图。最后，以“吉林1号”卫星视频中的不同城市道路场景为例，并比较了各种现有算法。实验证明，该算法在检测精度和召回率达到85％时，都能保持10％以下的误报率，并且在卫星角度偏差的图像环境中具有一定的抗干扰能力。通过结合有效邻域信息和帧间时间信息对检测结果进行优化，以输出二值目标检测图。最后，以“吉林1号”卫星视频中的不同城市道路场景为例，并比较了各种现有算法。实验证明，该算法在检测精度和召回率达到85％时，都能保持10％以下的误报率，并且在卫星角度偏差的图像环境中具有一定的抗干扰能力。通过结合有效邻域信息和帧间时间信息对检测结果进行优化，以输出二值目标检测图。最后，以“吉林1号”卫星视频中的不同城市道路场景为例，并比较了各种现有算法。实验证明，该算法在检测精度和召回率达到85％时，都能保持10％以下的误报率，并且在卫星角度偏差的图像环境中具有一定的抗干扰能力。