Segmentation And Tracking Of Moving Objects Based On MPM-MAP Frame

The segmentation and tracking of moving object are widely applied in many fields such as military affairs, weather, geography, commerce and media. They are very important research topics in pattern recognition, image processing and computer vision. The research status of moving object segmentation and tracking is analyzed and summarized in the dissertation. The approaches, achievements and problems of current works in this field are introduced then.The maximizer of the posterior marginals-maximum a posteriori (MPM-MAP) algorithm is adopted to implement moving object segmentation in the dissertation. MPM-MAP is developed on the basis of maximum posterior probability (MAP) algorithm. This kind of algorithms can both estimate motion parameters and segment moving objects. These algorithms are featured by their accurate results and wide applications. MPM-MAP algorithm makes it clear in the form that motion parameters and regions corresponding to the moving objects (supporting regions) are estimated in two steps. The binary labeling field is used to represent the supporting regions, which simplifies the calculation. The MPM-MAP algorithm is more flexible and faster than MAP and expectation maximization (EM) algorithms.With the MAP algorithm as the starting point, the theoretical frame of MPM-MAP algorithm is introduced in detail in the dissertation. The differences in MPM-MAP algorithm, MAP algorithm and EM algorithm are also analyzed in the dissertation. The smoothing algorithm in MPM-MAP based on Markov random (MRF) model is improved in the dissertation. In the improved MPM-MAP algorithm, the labeling field data are updated uniformly using the binary labeling representation and the MRF energy is computed by mean filter. Therefore, the improved algorithm heightens the speed of MRF algorithm while the smoothing effects remain the same as the traditional algorithms.Although the traditional MPM-MAP algorithm is much faster than MAP and EM algorithm, some shortcomings still remain in it. For example, it lacks of the effective approaches to estimate the number of moving objects and the initial motion parameters; the most often used optimal methods for motion parameter estimation are dependent on the initial values; the objective functions are required to be differentiable. MRP model has a good smoothing effect but it seems to be slow in running speed during the iterations even though for the fastest MRF algorithm.Aiming at the shortcomings mentioned above, some improvements are proposed in the dissertation. The smoothing term in MPM-MAP algorithm is changed and defined to be the density of pre-selected pixels belonging to the moving objects. The rectangular region with the maximum density is chosen as motion supporting region. The region shrinking algorithm is used to implement image smoothing and noise suppression. In addition to the noise suppression, this algorithm can locate the moving objects and compute the feature rectangles of the objects.The region shrinking algorithm has the advantage of higher speed than MRF algorithm. It can also be used in binary difference images as well as the estimation of supporting regions. The bounding boxes of connected regions are combined with region shrinking algorithm to estimate initial supporting regions. The original algorithm calculates the motion parameters first and then supporting regions. Supporting regions are computed first and then the motion parameters in the proposed algorithm. Therefore the initial parameters are more accurate than the traditional algorithm.The 6-parameter affine model is chosen as motion model in the dissertation. An axial affine model based on rectangular region is presented. This model describes affine motion by center translation, rotation and scaling of two main axes of a rectangular region. Its advantages are as follows. The first advantage is heightening the speed of motion estimation while keeping the accurate results as before. The second advantage is that it makes a clear geometric meaning for the affine parameters. The range of initial parameters is easy to be obtained. The optimal methods of parameter estimation can be chosen in searching approaches except the gradient-based and random optimal methods used before. A comparatively newer optimal approach with limited parameters—DIRECT algorithm is used to calculate the motion parameters. The method does not need a differentiable objective function and a set of assumed initial parameters. The combination of axis affine model and DIRECT algorithm improves the precision, stability and robustness of parameter estimation.The model and algorithms mentioned above are described in detail in the dissertation. Finally these algorithms are used in moving object tracking. The representation of rectangular region is adopted in tracking. The initial regions of objects and motion parameters are computed by the MPM-MAP algorithm based on region shrinking. The tracking approach based on kalman filter is presented in the dissertation. The state of kalman filter is a set of axial affine parameters. The covariance matrix of the observed vectors is computed by the interim results of DIRECT algorithm. The experiment results show that the combination of rectangular representation, axial affine model and DIRECT algorithm favors the better tracking of moving objects.The algorithms in the dissertation are simulated and proved to be feasible. The detailed experiment results are also given in the dissertation.