基于视觉的旋翼无人机地面目标跟踪(英文)
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scale-space extrema detection
I. INTRODUCTION UAV is one of the best platforms to perform dull, dirty or dangerous (3D) tasks [1]. UAV can be used in various applications where human is impossible to intervene. It greatly expands the application space of visual tracking. Research on the technology of vision based ground target tracking for UAV has been a great concern among cybernetic experts and robotic experts, and has become one of the most active research directions in UAV applications. Currently, researchers from America, Britain, France and Sweden are on the cutting edge in this field [2]. Typical visual tracking platforms for UAV include Scan Eagle, GTMax, RQ-11, RQ-16, DragonFly, etc. Because of many advantages, such as small size, light weight, flexible, easy to carry and low cost, rotor UAV has a broad application prospect in the fields of traffic monitoring, resource exploration, electricity patrol, forest fire prevention, aerial photography, atmospheric monitoring, etc [3]. Vision based ground target tracking system for rotor UAV is such a system that gets images by the camera installed on a low-flying rotor UAV, then recognizes the target in the images and estimates the motion state of the target, and finally according to the visual information regulates the pan-tilt-zoom (PTZ) camera automatically to keep the target at the center of the camera view. In view of the current situation of international researches, the study of ground target tracking system for
UAV is still in the stage of development and generally focuses on a certain unit technology which can be summarized as the following three aspects: (1) target recognition and state estimation based on vision, (2) flight control of UAV, (3) tracking control of the PTZ camera. This paper mainly studies the technology of target recognition and state estimation. Commonly used target tracking algorithms mainly include template matching, optical flow, meanshift, Kalman filter (KF), Particle filter (PF), and so on. From the viewpoint of control, the main difficulties faced by the visual tracking problems can be summed up with the requirements of tracking algorithm in three aspects, namely, robustness, accuracy and rapidity [4]. Compared with normal visual tracking systems, ground target tracking system for rotor UAV has a worse environment and higher requirements on tracking algorithm. In order to improve tracking robustness, the image matching algorithm based on SIFT feature is adopted to recognize the ground target in this paper. Kalman filter is utilized to estimate the motion state of the ground target to improve tracking rapidity. Finally, an experimental platform of visual tracking system for rotor UAV is set up to test the ground target tracking algorithm. II. TARGET RECOGNITION ALGORITHM SIFT algorithm is used to recognize the ground target in this paper. The SIFT algorithm, first proposed by David. G. Lowe in 1999 [5] and improved in 2004 [6], is a hot field of feature-matching at present, and its effectiveness is invariant of image rotation, scale zoom and brightness transformations, and also maintains a certain degree of stability on perspective transformation and affine transformation. SIFT feature points are scale-invariant local points of an image, with the characteristics of good uniqueness, informative, large amounts, high speed, scalability, and so on. A. SIFT Algorithm The SIFT algorithm consists of four parts. The process of SIFT feature construction is shown in Fig. 1.
2013 10th IEEE International Conference on Control and Automation (ICCA) Hangzhou, China, June 12-14, 2013
基于地面目标视觉跟踪的旋翼无人机
Vision Based Ground Target Tracking for Rotor UAV
Xuqiang ZHAO†*, Qing FEIБайду номын сангаас, Qingbo GENG†
Abstract— This paper studies an efficient ground target tracking algorithm for rotor Unmanned Aerial Vehicle (UAV) to overcome the contradiction among the target tracking rapidity, precision and robustness for aerial vehicle. Firstly, Scale Invariant Feature Transform (SIFT) algorithm, which has a better robust performance during rotation, scaling and changes of illumination, is utilized to extract and match the feature points in order to realize target recognition and positioning. Secondly, using top-down tracking method, Kalman filter is combined to estimate the target position in the next frame and search target in the predicted area, it can avoid blind matching, improve tracking rapidity and reduce the ratio of losing target. Finally, an experimental platform of rotor UAV visual tracking is set up and the ground target tracking algorithm is tested. The experiment results show that the algorithm can achieve ground target tracking effectively and has good real-time performance and robustness.
This work is supported by Deep Exploration Technology and Experimentation Project 201011080. † {Xuqiang ZHAO, Qing FEI, Qingbo GENG} are with School of Automation, Beijing Institute of Technology, Beijing, 100081, China. * To whom all correspondences should be addressed. Email: ace2011bit@
I. INTRODUCTION UAV is one of the best platforms to perform dull, dirty or dangerous (3D) tasks [1]. UAV can be used in various applications where human is impossible to intervene. It greatly expands the application space of visual tracking. Research on the technology of vision based ground target tracking for UAV has been a great concern among cybernetic experts and robotic experts, and has become one of the most active research directions in UAV applications. Currently, researchers from America, Britain, France and Sweden are on the cutting edge in this field [2]. Typical visual tracking platforms for UAV include Scan Eagle, GTMax, RQ-11, RQ-16, DragonFly, etc. Because of many advantages, such as small size, light weight, flexible, easy to carry and low cost, rotor UAV has a broad application prospect in the fields of traffic monitoring, resource exploration, electricity patrol, forest fire prevention, aerial photography, atmospheric monitoring, etc [3]. Vision based ground target tracking system for rotor UAV is such a system that gets images by the camera installed on a low-flying rotor UAV, then recognizes the target in the images and estimates the motion state of the target, and finally according to the visual information regulates the pan-tilt-zoom (PTZ) camera automatically to keep the target at the center of the camera view. In view of the current situation of international researches, the study of ground target tracking system for
UAV is still in the stage of development and generally focuses on a certain unit technology which can be summarized as the following three aspects: (1) target recognition and state estimation based on vision, (2) flight control of UAV, (3) tracking control of the PTZ camera. This paper mainly studies the technology of target recognition and state estimation. Commonly used target tracking algorithms mainly include template matching, optical flow, meanshift, Kalman filter (KF), Particle filter (PF), and so on. From the viewpoint of control, the main difficulties faced by the visual tracking problems can be summed up with the requirements of tracking algorithm in three aspects, namely, robustness, accuracy and rapidity [4]. Compared with normal visual tracking systems, ground target tracking system for rotor UAV has a worse environment and higher requirements on tracking algorithm. In order to improve tracking robustness, the image matching algorithm based on SIFT feature is adopted to recognize the ground target in this paper. Kalman filter is utilized to estimate the motion state of the ground target to improve tracking rapidity. Finally, an experimental platform of visual tracking system for rotor UAV is set up to test the ground target tracking algorithm. II. TARGET RECOGNITION ALGORITHM SIFT algorithm is used to recognize the ground target in this paper. The SIFT algorithm, first proposed by David. G. Lowe in 1999 [5] and improved in 2004 [6], is a hot field of feature-matching at present, and its effectiveness is invariant of image rotation, scale zoom and brightness transformations, and also maintains a certain degree of stability on perspective transformation and affine transformation. SIFT feature points are scale-invariant local points of an image, with the characteristics of good uniqueness, informative, large amounts, high speed, scalability, and so on. A. SIFT Algorithm The SIFT algorithm consists of four parts. The process of SIFT feature construction is shown in Fig. 1.
2013 10th IEEE International Conference on Control and Automation (ICCA) Hangzhou, China, June 12-14, 2013
基于地面目标视觉跟踪的旋翼无人机
Vision Based Ground Target Tracking for Rotor UAV
Xuqiang ZHAO†*, Qing FEIБайду номын сангаас, Qingbo GENG†
Abstract— This paper studies an efficient ground target tracking algorithm for rotor Unmanned Aerial Vehicle (UAV) to overcome the contradiction among the target tracking rapidity, precision and robustness for aerial vehicle. Firstly, Scale Invariant Feature Transform (SIFT) algorithm, which has a better robust performance during rotation, scaling and changes of illumination, is utilized to extract and match the feature points in order to realize target recognition and positioning. Secondly, using top-down tracking method, Kalman filter is combined to estimate the target position in the next frame and search target in the predicted area, it can avoid blind matching, improve tracking rapidity and reduce the ratio of losing target. Finally, an experimental platform of rotor UAV visual tracking is set up and the ground target tracking algorithm is tested. The experiment results show that the algorithm can achieve ground target tracking effectively and has good real-time performance and robustness.
This work is supported by Deep Exploration Technology and Experimentation Project 201011080. † {Xuqiang ZHAO, Qing FEI, Qingbo GENG} are with School of Automation, Beijing Institute of Technology, Beijing, 100081, China. * To whom all correspondences should be addressed. Email: ace2011bit@