外文翻译运动图像和运动矢量检测综述.docx

1、外文翻译运动图像和运动矢量检测综述外文文献：A SURVEY ON MOTION IMAGE AND THE SEARCH OF MOTION VECTORAfter 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 proces

2、sing. 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

3、 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 Track

4、ingRegion-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

5、 et al. 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 sce

6、ne are modeled 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 p

7、ropose an adaptive 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 regi

8、ons can merge and 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 mo

9、del, perfect tracking 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 PropulsionLaboratory (JPL), and the PA

10、TH system developed 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 a

11、re essentially 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 wi

12、th multiple moving objects.B. Active Contour-Based TrackingActive 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 provi

13、de more effective 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

14、 filter for tracking nonrigid moving 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 activ

15、e contour-based 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 continu

16、ously. However, 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, makin

17、g it difficult to start tracking automatically.C. Feature-Based TrackingFeature-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 ca

18、n further 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 order

19、s of quadratures 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

20、centroids 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

21、 between features.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 e

22、stimation 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 rea

23、l-time processing and tracking of multiple objects which are required in heavy thruway scenes, etc. However, dependence-graph-based algorithms cannot be used in real-time tracking because they need time-consuming searching and matching of graphs. Feature-based tracking algorithms can handle partial

24、occlusion by using information on object motion, local features and dependence graphs. However, there are several serious deficiencies in feature-based tracking algorithms. The recognition rate of objects based on 2-D image features is low, because of the nonlinear distortion during perspective proj

25、ection and the image variations with the viewpoints movement. These algorithms are generally unable to recover 3-D pose of objects. The stability of dealing effectively with occlusion, overlapping and interference of unrelated structures is generally poor.D. Model-Based TrackingModel-based tracking

26、algorithms track objects by matching projected object models, produced with prior knowledge, to image data. The models are usually constructed off-line with manual measurement, CAD tools or computer vision techniques. As model-based rigid object tracking and model-based no rigid object tracking are

27、quite different, we review separately model-based human body tracking (no rigid object tracking) and model-based vehicle tracking (rigid object tracking).1.Model-Based Human Body Tracking:The general approach for model-based human body tracing is known as analysis-by-synthesis, and it is used in a p

28、redict-match-update style. Firstly, the pose of the model for the next frame is predicted according to prior knowledge and tracking history. Then, the predicted model is synthesized and projected into the image plane for comparison with the image data. A specific pose evaluation function is needed t

29、o measure the similarity between the projected model and the image data. According to different search strategies, this is done either recursively or using sampling techniques until the correct pose is finally found and is used to update the model. Pose estimation in the first frame needs to be hand

30、led specially. Generally, model-based human body tracking involves three main issues: Construction of human body models； Representation of prior knowledge of motion models and motion constraints； Prediction and search strategies. Previous work on these three issues is briefly and respectively review

31、ed as follows.AHuman body models: Construction of human body models is the base of model-based human body tracking. Generally, the more complex a human body model, the more accurate the tracking results, but the more expensive the computation. Traditionally, the geometric structure of human body can

32、 be represented in the following four styles. Stick figure. The essence of human motion is typically contained in the movements of the torso, the head and the four limbs, so the stick-figure method is to represent the parts of a human body as sticks and link the sticks with joints. Karaulova et al.

33、use a stick figure representation to build a novel hierarchical model of human dynamics encoded using hidden Markov models (HMMs), and realize view-independent tracking of a human body in monocular image sequences. 2-D contour. This kind of human body model is directly relevant to human body projections in an image plane