Abstract
This thesis describes our exploration of constructing a visual tracking system based on fast keypoint matching. To achieve real-time performance, the ferns architecture, which is an improvement of the randomized trees architecture and is simpler and faster in training, testing, and implementing, is adapted in our thesis for building classifiers. We separate the experiment into two phases: i) off-line training phase and ii) tracking phase. In the off-line training phase, we train independent-feature and joint-feature classifiers separately according to their infinite training sets which are created from different kinds of transformation matrices. To enhance the recognition accuracy, we incorporate the co-occurrence information of keypoints and use a graph-based representation to model the spatial relations between the keypoints. More specifically, we build a minimum spanning tree on the keypoints using Prim’s algorithm. We introduce the joint-feature ferns classifiers that take account of the spatial relations between keypoints and thus improve the accuracy of keypoint recognition. Then, we sequentially rebuild the minimum spanning tree on the new frame. As for the occlusion problem, we try to rebuild partial sets of minimum spanning tree. As a result, we can increase the overall recognition accuracy efficiently by combining independent feature recognition with joint feature recognition. Furthermore, to speed up the time-consuming off-line training process, we present a novel scheme called large-feature-set training to avoid the need of intensive image transformations. The time required for training ferns classifiers can be reduced to about 10%.