Study On Tracking Algorithm Based On Semi-supervised Online Learning

Object tracking is an attractive and challenging subject of computer vision, draws a lot of attention. Solving tracking problem with machine learning technique is a new rising area of research, also indicates the trend of tracking research in the future. In this thesis, we mainly study the application of semi-supervised learning in object tracking area, especially the one based on structural constraints. It's organized as follows:1. Semi-supervised learning algorithm Several popular semi-supervised learning algorithms are studied, including semi-boosting, multiple instance learning, and P-N learning. Main attention is paid to P-N learning, especially its performance in object detection and tracking area.2. Research on tracking algorithm We investigate several popular tracking algorithms, including optical flow, particle filter on affine group, and generalized hough transform. Optical flow and particle filter on affine group are incorporated in our semi-supervised learning based tracking framework. Moreover, A new approach for tracking multiple objects is proposed, which combines feature correspondence with a probabilistic appearance model, and uses generalized hough transform to determine the optimal target position.3. Design and implementation of tracking system based on semi-supervised learningTemporal, spatial and data correlation constraints are designed to intervene extraction of training data, training of classifiers and labeling of unlabeled data, which improves the performance of classifiers. Optical flow and particle filter on affine group are incorporated in our tracking system framework. Experiments show that our tracking system work well under challenging situation, including fast moving, large scale rotation, object reappear and occlusion with similar object.