葛宝义,左宪章,胡永江(陆军工程大学无人机工程系, 石家庄 050003)
目的 随着军事侦察任务设备的发展，红外与可见光侦察技术成为军事装备中的主要侦察手段。研究视觉目标跟踪技术对提高任务设备的全天候目标侦察、目标跟踪、目标定位等战场情报获取能力具有重要意义。目前，对视觉目标跟踪技术的研究越来越深入，目标跟踪的方法和种类也越来越丰富。本文对目前应用较为广泛的4种视觉目标跟踪方法进行研究综述，为后续国内外研究者对目标跟踪相关理论及发展研究工作提供基础。方法 通过对视觉目标跟踪技术难点问题进行分析，根据目标跟踪方法建模方式的不同，将视觉目标跟踪方法分为生成式模型方法与判别式模型方法。分别对生成式模型跟踪算法中的均值漂移目标跟踪方法和粒子滤波目标跟踪方法，判别式模型跟踪算法中的相关滤波目标跟踪方法和深度学习目标跟踪方法进行研究。首先分别对4种跟踪算法的基本原理进行介绍，然后针对4种跟踪算法基本原理的不足和对应目标跟踪中的难点问题进行分析，最后针对目标跟踪的难点问题，给出对应算法的主流改进方案。结果 针对视觉目标跟踪相关技术研究进展，结合无人机侦察任务需求，对跟踪算法实际应用中存在的重点解决问题与相关目标跟踪的难点问题进行分析，给出目前的解决方案与不足，探讨研究未来无人机目标侦察跟踪技术的发展方向。结论 视觉目标跟踪技术已经取得了显著的进展，在侦察任务中的应用越来越广泛。但目标跟踪技术仍然是非常具有挑战性的问题，目标跟踪中的相关理论有待进一步完善和改进，由于实际应用中的场景复杂，目标跟踪的难点问题的挑战性更大，因此容易导致跟踪效果不佳。针对不同的应用环境，结合具体不同军事装备的特点，研究相对精确和鲁棒并且满足实时性要求的视觉目标跟踪算法，对提升装备的全天候侦察目标信息获取能力具有重要意义。
Review of visual object tracking technology
Ge Baoyi,Zuo Xianzhang,Hu Yongjiang(Department of Unmanned Aerial Vehicle Engineering, Army Engineering University, Shijiazhuang 050003, China)
Objective With the development of military reconnaissance mission equipment, infrared and visible light target reconnaissance techniques have already become the main means of reconnaissance among military equipment. Research on infrared and visible light object tracking technology is important for the improvement of intelligence equipment related to battlefield acquisition and precision strike in military missions, such as all-weather target reconnaissance, object tracking, and target location. Presently, with the rise of computer vision technology, visual object tracking technology has gradually become the focus and challenge of research, and the methods and kinds of object tracking techniques are increasing. In this study, four kinds of visual object tracking methods, which are extensively used at present, are reviewed. This work serves as basis for follow-up research on the theory and development of object tracking. Method By analyzing the difficult problems of infrared and visible object tracking technology, the visual object tracking method is divided into generative and discriminative model methods, the different modeling methods of object tracking. The mean shift and particle filter object tracking in generative model algorithm and the correlation filtering and deep learning object tracking in discriminative model algorithm are reviewed in this paper. First, the basic principles of the three standard object tracking algorithms, namely, mean shift object tracking and particle filter object tracking methods and correlation filters for object tracking method, are comprehensively analyzed. Then, the limitations of the basic principles of the three tracking algorithms are listed, and the corresponding difficulties in object tracking that need to be solved are presented. By analyzing the difficult problems in object tracking, the mainstream improvement scheme of the corresponding object tracking algorithm is given. According to the characteristics of infrared image and the difficult problem of infrared object tracking, the improved algorithm of infrared correlation filter for object tracking is presented. We analyzed the methods of object tracking using deep learning and divided them into two categories. One is to take the neural network feature as the target feature extraction method. We analyzed its feature extraction principles and characteristics and feature extraction strategy in object tracking. Moreover, the corresponding improvement scheme is also provided according to the characteristics of infrared object tracking. The other one is the neural network framework. We summarized its principles and characteristics and analyzed its various architecture advantages and disadvantages in object tracking. To address the problem of infrared object tracking, an improvement scheme is proposed. Finally, we summarized the present situation and discussed the practical application and future development trend of object tracking technology. Result Presently, the visual object tracking technology has a reliable performance under short-term object tracking condition. However, in long-term tracking required in practical application is difficult because the application scene is complex, making the difficult problem of object tracking prominent. Given the key and difficult problems in object tracking, such as target occlusion and target out of view, the robustness and precision of object tracking technology are required to be high in practical application, and corresponding solutions to the problem of long-time object tracking should be put forward. In view of the progress in the research on technology related to visual object tracking, along with the demand of unmanned aerial vehicle reconnaissance mission and the high maneuverability of unmanned aerial vehicles, this study analyzes the key problems, gives the current solutions of the existing weaknesses and explores the development direction. Conclusion Thus far, the visual object tracking technology has performed remarkable progress, and its accuracy and success rate have been significantly improved. Visual object tracking technology is becoming widely used in the reconnaissance missions of military equipment. However, the technology of object tracking remains challenging. The related theories of object tracking need to be further tested and improved, especially in view of the characteristics of infrared object tracking. To improve the object tracking effect in infrared image, the corresponding object tracking method and improved scheme should be further studied. The object tracking is challenging because the application scene is complex. The robustness and accuracy of the object tracking algorithm should be high to avoid failure, and its real-time performance and tracking speed should meet real-time requirements. Considering the application characteristics and application scope of different military equipment, finding a visual object tracking algorithm is important. The algorithm must be relatively accurate and robust and meets real-time requirements to enhance the equipment's all-weather reconnaissance ability and target battlefield information acquisition capability.