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

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内容摘要：

rame. Now difference of the current frame and transformed previous frame reveals true moving targets. Then we apply a threshold to produce a binary image. The results of the transformation and segmentation are shown is figure 1~a and 1~b. Some parts are segmented as moving targets due to noise. Connected ponent property can be applied to reduce errors due to noise. We use split and merge algorithm to find target boundingboxes. 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 the target by adjusting the frame rate of the camera. The algorithm acplishes this automatically by analyzing the proceeding frames until a target is detected. Our special interest is detection and tracking of the moving vehicles so we used aspect ratio and horizontal and vertical line as constraints to verify vehicles. Our experiments show that parison of the length of horizontal and vertical lines in the target area with the perimeter of the target will give a good clue about the nature of the target. 3 Target tracking After a target is verified, the algorithm switches into the tracking mode. Modified Moravec operator is applied to the target to identify feature points. These feature points are matched with points in the region of interest in the current frame. Disparity vectors are puted for the matched pairs of points. We used disparity vectors to refine the matched points. The refined points define the new position of the target in the current frame. The algorithm switches to the detection mode whenever the target is missed. Although the detection algorithm described above can be used for tracking too but the tracking algorithm, we describe in this section has very low putation cost in contrast with the detection algorithm described above. On the other hand when the target is detected it is not restricted to keep moving in tracking mode. The target can also be larger than 50% of the scenery in the tracking mode and this means camera can zoom to have a larger view of the target while tracking. Figure 1: two consecutive frames and difference of them after background motion pensation, the calculated affine parameters are: a1=, a2=, a3=, a4=, a5=, a6= When the size of the target is fixed the normalized cross~correlation or SSD (Sum of Squared Differences) method can be directly applied for target tracking. But we don’t restrict the target to have a fixed size. Our tracking algorithm is capable of updating the target shape and size. To achieve this goal, the algorithm is based on dynamic feature points tracking. We select feature points from the target area and we track them in the next frame. Horizontal and vertical lines are important features for vehicle tracking. So we used optimized Moravec operator, which selects feature points, considering only horizontal and vertical gradients. This improves selection of interesting feature points located on the geometrically welldefined structures such as vehicles. This feature is very useful when dealing with occlusion. Our tracking algorithm consists of four steps, described as follow. 1. Apply the modified Moravec algorithm to select feature points in the target area in the previous frame. 2. Find corresponds of the feature points in the ROI (region of interest) of the current frame using normalized crosscorrelation. 3. Calculate the disparity vectors and based on these vectors refine feature points. The refining is defined as omitting features with inconsistent vectors. This helps removal of non~target feature points. 4. Based on the location of refined corresponding points and the previous size of the target determine the location and size of the target in the current frame. To refine the feature points described in the step 3, we calculated the mean and variance of the。