The Research On Multiple Objects Tracking Based On Image Sequences

The thesis is focused on the technique of the multiple objects tracking based on image sequences. As a widely used technology, tracking of objects from image sequences has been given attention by many experts. Although a number of algorithms have been proposed, the applicable ones are few. Most of them need further improving. Under these conditions, the aim of this thesis is to study image feature extraction, image stabilization and tracking algorithms systematically, to explore and summarize an applicable objects tracking scheme, based on the available research work. Then, with a better understanding, we hope to make some improvements.For the detecting and tracking of objects in a static background, we summarize mature detecting algorithms, compare the adjacent frame difference method with the adaptive background difference one and implement an automatic object detecting and tracking system based on adaptive background difference and Kalman filtration.As for image feature extraction, we systematically summarize available extraction methods and present the definition of image feature points. After the careful study of Gabor wavelet based feature extraction technique, we propose an extraction method suitable for image stabilization. On the proving ground, the extracted feature points contain the precise displacement between two adjacent frames, thus form a credible basis for the image stabilization.Having precisely extracted feature points, we go into the image stabilization technique based on the Gaussian pyramid. The pairwise matching of feature points in each resolution is the emphasis of our discussion. An improved method based on mapping is adopted to find the best match of each feature point. Experimental results show that it can effectively reduce the number of wrong matches, thus increase the precision of image stabilization.Starting with the characteristics of moving objects in image sequences, we go ahead to study the requirements of robust tracking algorithms on the selected object features. Particular study of Mean Shift algorithm leads to an improvement of it: we change the bandwidth parameter h from a scalar to a vector. Experiments with several real sequences show the better adaptability of the improvement to the complex movements of objects with the ability to adjust the target regions in two independent directions and no additional computational cost. Finally, based on the analysis of the commonness of various algorithms, we design and implement a software framework to test and develop tracking algorithms.