1、外文文献： 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 o
2、ver 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 fo
3、ur 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-base
4、d 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
5、 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
6、with Gaussian distributions of pixel values. Finally, the pixels belonging to the human body are assigned to the different body parts 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 adapt
7、ive background subtraction method in which color and gradient information are combined to cope with shadows and unreliable color cues in motion segmentation. Tracking is then performed at three levels of abstraction: regions, people, and groups. Each region has a bounding box and regions can merge a
8、nd split. A human is composed of one or more regions grouped together under the condition of geometric structure constraints on the human body, and a human group consists of one or more people grouped together. Therefore, using the region tracker and the individual color appearance model, perfect tr
9、acking of multiple people is achieved, even during occlusion. As far as region-based vehicle tracking is concerned, there are some typical systems such as the CMS mobilized system supported by the Federal Highway Administration (FHWA), at the Jet Propulsion Laboratory (JPL), and the PATH system deve
10、loped by the Berkeley group. Although they work well in scenes containing only a few objects (such as highways), region-based tracking algorithms cannot reliably handle occlusion between objects. Furthermore, as these algorithms only obtain the tracking results at the region level and are essentiall
11、y procedures for motion detection, the outline or 3-D pose of objects cannot be acquired. (The 3-D pose of an object consists of the position and orientation of the object).Accordingly, these algorithms cannot satisfy the requirement for surveillance against a cluttered background or with multiple m
12、oving objects. B. Active Contour-Based Tracking Active contour-based tracking algorithms track objects by representing their outlines as bounding contours and updating these contours dynamically in successive frames. These algorithms aim at directly extracting shapes of subjects and provide more eff
13、ective descriptions of objects than region-based algorithms. Paragios et al. detect and track multiple moving objects in image sequences using a geodesic active contour objective function and a level set formulation scheme. Peterfreund explores a new active contour model based on a Kalman filter for
14、 tracking nonrigidmoving targets such as people in spatio-velocity space. Isard et al. adopt stochastic differential equations to describe complex motion models, and combine this approach with deformable templates to cope with people tracking. Malik et al. have successfully applied active contour-ba
15、sed methods to vehicle tracking. In contrast to region-based tracking algorithms, active contour-based algorithms describe objects more simply and more effectively and reduce computational complexity. Even under disturbance or partial occlusion, these algorithms may track objects continuously. Howev
16、er, the tracking precision is limited at the contour level. The recovery of the 3-D pose of an object from its contour on the image plane is a demanding problem. A further difficulty is that the active contour-based algorithms are highly sensitive to the initialization of tracking, making it difficu
17、lt to start tracking automatically. C. Feature-Based Tracking Feature-based tracking algorithms perform recognition and tracking of objects by extracting elements, clustering them into higher level features and then matching the features between images. Feature-based tracking algorithms can further
18、be classified into three subcategories according to the nature of selected features: global feature-based algorithms, local feature-based algorithms, and dependence-graph-based algorithms. The features used in global feature-based algorithms include centroids, perimeters, areas, some orders of quadr
19、atures and colors, etc. Polana et al. provide a good example of global feature-based tracking. A person is bounded with a rectangular box whose centroid is selected as the feature for tracking. Even when occlusion happens between two persons during tracking, as long as the velocity of the centroids
20、can be distinguished effectively, tracking is still successful. The features used in local feature-based algorithms include line segments, curve segments, and corner vertices, etc. The features used in dependence-graph-based algorithms include a variety of distances and geometric relations between f
21、eatures. The above three methods can be combined .In there cent work of Jang et al. 34, an active template that characterizes regional and structural features of an object is built dynamically based on the information of shape, texture, color, and edge features of the region. Using motion estimation
22、 based on a Kalman filter,the tracking of a nonrigid moving object is successfully performed by minimizing a feature energy function during the matching process. In general, as they operate on 2-D image planes, feature-based tracking algorithms can adapt successfully and rapidly to allow real-time processing and tracking of multiple objects
