外文 英文 翻译 光学 毕业设计 运动图像和运动矢量检测综述.doc

外文 英文 翻译 光学 毕业设计 、运动图像和运动矢量检测综述 A SURVEY ON MOTION IMAGE ANDTHE SEARCH OF MOTION VECTOR 　　　After motion detection, surveillance systems generally track moving objects from one frame to another in an image sequence. The tracking algorithms usually have considerable intersection with motion detection during processing. Tracking over time typically involves matching objects in consecutive frames using features such as points, lines or blobs. Useful mathematical tools for tracking include the Kalman filter, the Condensation algorithm, the dynamic Bayesian network, the geodesic method, etc. Tracking methods are divided into four major categories: region-based tracking, active-contour-based tracking, feature based tracking, and model-based tracking. It should be pointed out that this classification is not absolute in that algorithms from different categories can be integrated together. A. Region-Based Tracking 　　Region-based tracking algorithms track objects according to variations of the image regions corresponding to the moving objects. For these algorithms, the background image is maintained dynamically, and motion regions are usually detected by subtracting the background from the current image. Wren etal. explore the use of small blob features to track a single human in an indoor environment. In their work, a human body is considered as a combination of some blobs respectively representing various body parts such as head, torso and the four limbs. Meanwhile, both human body and background scene are modeled with Gaussian distributions of pixel values. Finally, the pixels belonging to the human body are assigned to the different body part’s blobs using the log-likelihood measure. Therefore, by tracking each small blob, the moving human is successfully tracked. Recently, McKenna et al. [11] propose an adaptive background subtraction method in which color and gradient information are combined to cope with shadows and unreliable color cues in motion segmentation. Trackin