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无人机平台下运动目标的快速精确检测技术是无人机自主化的关键所在。无论在目标侦察、火力打击等军事领域，还是在智能交通、警用安防等民用领域，该技术都有着举足轻重的作用。现有无人机平台下的运动目标检测算法都是针对低分辨率视频设计的，且算法在实时性、环境适应性和目标提取精度等方面都有待提升。为实现对大范围区域的精细监视，近年来侦察无人机多采用高分辨率相机。然而，受限于无人机的机载计算资源，我们只能利用有限几帧高分辨率航拍图像进行检测，导致现有基于视频分析的方法（需要大量图像帧进行检测）不再适用。针对上述问题，本文对航拍视频，尤其是高分辨率（高分）航拍图像序列运动目标检测技术展开了深入的研究。论文工作主要包括以下三个部分：

 

（1）针对移动平台下基于背景建模的方法存在累积校正误差与视差等问题，提出了一种基于在线动态背景建模的航拍视频运动目标检测方法。所提方法能够通过在当前图像帧与动态背景模型之间进行校正，减少校正误差与视差的累积；针对动态背景图像模糊且不含运动信息的特点，提出了采用基于跟踪的匹配策略来对动态背景图像与当前图像帧进行快速精确地配准；针对无人机频繁摇晃或徘徊监视等情况，提出建立具有记忆功能的动态背景模型来更好地处理视野边缘的目标与背景。实验表明所提算法能够在保证算法效率的基础上实现对运动目标更为完整精确地提取，且具有很强的环境适应能力。

 

（2）针对目前无人机平台下的运动目标检测算法都是基于低分辨率视频设计的、往往需要很多帧图像、实时性差、无法解决高分辨率场景下的运动目标检测等问题，提出了一种基于特征分类的高分航拍图像序列运动目标检测方法。所提方法通过在特征匹配点对空间中进行检测代替直接在像素空间中进行检测，大大提升了算法的检测效率；设计并实现了整个基于特征分类的运动目标检测方法，包括特征匹配对的提取与修正、特征匹配对的分类和基于一致性的聚类等几个部分；提出了基于跟踪的分块定量提取策略和基于一致性与块匹配的特征匹配对修正算法，并设计了用于区分运动目标上匹配对与背景匹配对的有效特征和用于聚类的一致性准则。实验验证了算法设计的有效性，实验结果表明所提算法只需利用两帧图像即可实现对运动目标的快速准确指示与定位；算法有着极高的计算效率，可以为跟踪、识别等高层视觉任务留出宝贵的机载计算资源。

 

（3）针对运动目标检测中常出现的只检测到目标部分区域（部分检）、将一个运动目标检测为多个运动目标（一检多）和将多个运动目标检测为一个运动目标（多检一）等问题，在基于特征分类的运动目标检测方法基础上，提出了一种基于增量特征组织与自导向分割的高分航拍图像序列运动目标检测方法。所提方法通过基于增量特征组织的扩充与合并算法，充分利用时域信息来解决部分检和一检多的问题；通过基于GrabCut的自导向分割算法，充分利用空域信息来解决多检一和部分检的问题。实验表明，所提算法能够有效地解决部分检、一检多和多检一等问题，在保证较高计算效率的同时大大提升了对运动目标的完整精确提取能力。

 

论文工作涵盖了低分辨率和高分辨率航拍视频/图像序列中的运动目标检测，共同完善了无人机平台下运动目标检测的应用方法，并填补了高分航拍图像序列运动目标检测方法上的空白，为后续机载应用打下了坚实的基础。

The fast and accurate moving object detection under the unmanned aerial vehicles (UAV) platform is the key to the intelligence of UAV. It plays a very important role, no matter in target reconnaissance, fire attack and other military applications or intelligent transportation, police security and other civilian areas. The previous works on moving object detection from aerial videos are mainly designed for low-resolution videos and there are still many aspects need to be improved, such as the real-time capability, the adaptability to environment and the detection accuracy. Recently, to achieve a wide-area of surveillance with fine granularity, most of the UAVs prefer capturing high-resolution videos but can only process a few frames of the videos due to constraint from computing resource, making the existing video-based algorithms (need many continuous frames for detection) unsuitable for the high-resolution scenario. To solve these problems, this paper carries out systematic research on moving object detection from aerial videos, especially from the high-resolution aerial videos. Our main works include the following three parts:

 

(1) Considering that most of the existing background modeling based moving object detection algorithms suffer from the accumulation of the stabilization errors and parallax interferences, we propose a dynamic online background modeling based algorithm. By making stabilization between the current frame and the dynamic background model, we can reduce the accumulation of the stabilization errors. Considering that the dynamic background image is fuzzy and does not contain the information of moving targets, we propose to use a tracking based matching strategy to stabilize the background image and the current frame fast and accurately. In considering the frequent shaking of the UAV and the wandering detection situation, we build a background model which can record the information of the pixels near the frame boundary and better handle the objects and background near the fringe field of view. Experiments show that the proposed method can detect the moving objects more accurately with a high computational efficiency. Besides, it has a high adaptability to environments.

 

(2) In view of the existing moving object detection algorithms under the UAV platform are all designed for low-resolution videos, which often require many frames and are difficult to detect the moving targets in real-time, cannot tackle the motion detection task for high-resolution situation, we propose a feature classification based method for online moving object detection from high-resolution aerial image sequences. The proposed method detects the moving objects by fully utilizing the motion information of the feature pairs between two frames. By performing the detection in the feature pair domain instead of in the pixel domain, the proposed method has a very high computational efficiency. We design a whole detection system, which includes feature extraction and refinement, feature classification and consistency based clustering. We propose a tracking-based block extraction strategy and a feature matching pairing algorithm base on consistency and block matching, and design valid features for distinguishing the pairs on the moving objects from background pairs and design a consistency criteria for clustering. Experiments are made to validate the effectiveness of the proposed method. Experimental results show that the proposed algorithm can detect and locate the moving targets fast and accurately. The proposed algorithm has a very high computational efficiency and can leave valuable onboard computing resources for high-level vision tasks. 

 

(3) Taking into account the common issues of detecting only part of the moving target, detecting one target as multiple targets and detecting multiple targets as one target for moving object detection algorithms, we propose an incremental feature organization and self-directed segmentation based method for moving object detection from high-resolution aerial image sequences. By the incremental feature organization based expansion and merging algorithm, time domain information is fully used to tackle the problems of part detection and detecting one target as multiple targets. By the GrabCut based self-directed segmentation algorithm, the spatial domain information is amply utilized to handle the problems of detecting multiple targets as one target and part detection. Experiments show that, the proposed method can better solve the problems of part detection, detecting one target as multiple targets and detecting multiple targets as one. With high computational efficiency, the proposed algorithm can greatly improve the performance of completely and accurately extracting the moving objects.

 

The works of this paper cover moving object detection from low-resolution and high-resolution aerial videos and they constitute a complete system for moving object detection under UAV platform. To our best knowledge, these works tackle the moving object detection form high-resolution aerial image sequences for the first time, which lay a solid foundation for embedding the moving object detection system into the hardware platform for practical applications.