外文翻译---一个鲁棒的基于机器视觉运动目标检测与跟踪系统

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1、PDF外文：http:/ I 外文文献翻译  (1)原文：  A Robust Vision-based Moving Target Detection and Tracking System  Abstract    In this paper we present a new algorithm for realtime detection and tracking of moving targets in terrestrial scenes using a mobile camera. Our algorithm consists of

2、 two modes: detection and tracking. In the detection mode, background motion is estimated and compensated using an affine transformation. The  resultant motion rectified image is used for detection of the target location using split and merge algorithm. We also checked other features for precis

3、e detection of the target location. When the target is identified, algorithm switches to the tracking mode. Modified Moravec operator is applied to the target to identify feature points. The feature points are matched with points in the region of interest in the current frame. The corresponding poin

4、ts are further refined using disparity vectors. The tracking system is capable of target shape recovery and therefore it can successfully track targets with varying distance from camera or while the camera is zooming. Local and regional computations have made the algorithm suitable for real-time app

5、lications. The refined points define the new position of the target in the current frame. Experimental results have  shown that the algorithm is reliable and can successfully detect and track targets in most cases.  Key words: real time moving target tracking and detection, feature matchin

6、g, affine transformation, vehicle tracking, mobile camera image. 1 Introduction     Visual detection and tracking is one of the most challenging issues in computer vision. Application of the visual detection and tracking are numerous and they span a wide range of applications including sur

7、veillance system, vehicle tracking and aerospace application, to name a few. Detection and tracking of abstract targets (e.g. vehicles in general) is a very complex problem and demands sophisticated solutions using conventional pattern recognition and motion estimation methods. Motion-based segmenta

8、tion is one of the powerful tools for detection and tracking of moving targets. It is simple to detect moving objects in image sequences obtained by stationary camera 1, 2, the conventional difference-based methods fail to detect moving targets when the camera is also moving. In the case of mobile c

9、amera all of the objects in the image sequence have an apparent motion, which is related to the camera motion. A number of methods have been proposed for detection of the moving targets in mobile camera including direct camera motion parameters estimation 3, optical flow 4, 5, and geometric transfor

10、mation 6, 7. Direct measurement of camera motion parameters is the best method for cancellation of the apparent background motion but in some application it is not possible to measure these parameters directly. Geometric transformation methods have low computation cost and are suitable for realtime

11、purpose. In these methods, a uniform background motion is assumed. An affine motion model could be used to model this motion. When the apparent motion of the background is estimated, it can be exploited to locate moving objects.  In this paper we propose a new method for detection and tracking

12、of moving targets using a mobile monocular camera. Our algorithm has two modes: detection and tracking. This paper is organized as follows. In Section 2, the detection procedure is discussed. Section 3 describes the tracking method. Experimental results are shown in Section 4 and conclusion appears

13、in Section 5.   2 Target detection    In the detection mode we used affine transformation and LMedS (Least median squared) method for robust estimation of the apparent background motion. After the compensation of the background motion, we apply split and merge algorithm to the differe

14、nce of current frame and the transformed previous frame to obtain an estimation of the target positions. If no target is found, then it means either there is no moving target in the scene or, the relative motion of the target is too small to be detected. In the latter case, it is possible to detect

15、the target by adjusting the frame rate of the camera. The algorithm accomplishes this automatically by analyzing the proceeding frames until a major difference is detected.  We designed a voting method to verify the targets based on apriori knowledge of the targets. For the case of vehicle dete

16、ction we used vertical and horizontal gradients to locate interesting features as well as constraint on area of the target as discussed in this section. 2.1 Background motion estimation    Affine transformation 8 has been used to model motion of the camera. This model includes rotation, sc

17、aling and translation. 2D affine transformation is described as follow:   aayxaa aaYXiiii654321                  (1) where (xi , yi ) are locations of points in the previous frame and (Xi , Yi ) are locations of points in the current frame and a1a6 a

18、re motion parameters. This transformation has six parameters； therefore, three matching pairs are required to fully recover the motion. It is necessary to select the three points from the stationary background to assure an accurate model for camera motion. We used Moravec operator 9 to find distingu

19、ished feature points to ensure precise match. Moravec operator selects pixels with the maximum directional gradient in the minmax sense.  If the moving targets constitute a small area (i.e. less than 50%) of the image, then LMedS algorithm can be applied to determine the affine transformation p

20、arameters of the apparent background motion between two consecutive frames according to the following procedure.  1. Select N random feature point from previous frame, and use the standard normalized cross correlation method to locate the corresponding points in the current frame. Normalized co

21、rrelation equation is given by:  21, ,222211,2211),(),(),(),( Syx SyxSyxfyxyxyxyxrfffffff          (2) here 1f and 2f  are the average intensities of the pixels in the two regions being compared, and the summations are carried out over all pixels with in small windows centered on the feature points. The value r in the above equation measures the similarity between two regions and is between 1 and -1. Since it is assumed that moving objects are less than 50% of the whole image, therefore most of the N points will belong to the stationary background.