Research On Techniques In Scene Registration And Object Surveillance For UAV Imagery

Basing on the exigent requirements of the real-time object surveillance of dynamic environment for unmanned aerial vehicle(UAV), and aiming at applications of object location and the scene property acquired by UAV, this dissertation makes a study on several key techniques for optical and infrared sequential imagery, such as invariability matching, object orientation of affine distortion, image registration across scenes, motion object detection and tracking,etc. The works achieved in this dissertation are mainly as follow:1,The components and the process of object surveillance technique for UAV is analyzed, with the framework designed too.2,The theoretics of each matching method of sparse local invariant features is analyzed. Aiming at characteristic of applications for UAV sequential imagery, the space distribution controlling method is proposed to adjust the parameters of SIFT, which makes the matching space more uniform. The improved k-d tree method is adopted to accelerate processing. Each matching method is implemented, and the advantage and disadvantage of each method are analyzed with the experimental results.3,A method of simulating longitude and latitude based on ASIFT is proposed, in order to find the orientation of object in oblique image. The procedure selects the affine transforms that yielded matches in the low-resolution process, then simulates the selected affine transforms on the original query and search images, and finally compares the simulated images by SIFT. The effect of affine distortion by great tilt of camera axes on UAV can be overcome, and the stabilization of object orientation of sequence imagery can be guaranteed, too.4,SIFT flow is introduced into the method for registering an image to its nearest neighbors in a large image corpus containing a variety of scenes. The SIFT flow algorithm consists of matching densely sampled, pixel-wise SIFT features between two images, while preserving spatial discontinuities, accomplishing the correspondence densely across scenes of coarse-to-fine. Experiments of registration across scenes of UAV sequential imagery prove that this method can complete assignments which the tradition method is not able to.5,A method that combines center point featrue descriptor and MeanShiftis is proposed. According to the noise characteristics of infrared image, the wavelet method is used to reduce the noise of infrared image. After registering with SURF matching method, the motion object can be predicted by Kalman filtering. A new histogram can be made by combining the color histogram and center descriptor extracted in search area, then with MeanShift modifying, object tracking can be achieved. Also, the problem of object blocked and size changed is solved in this dissertation.6,The principal of Particle Filtering is studied. An approach of combining two well-developed algorithms: mixture particle filters and Adaboost, is adopted. The learned Adaboost proposal distribution allows us to quickly detect object, while the filtering process enables us to keep track of the motion object. We construct the proposal distribution using a mixture model that incorporates information from the dynamic models of object and the detection hypotheses generated by Adaboost. The result of interleaving Adaboost with mixture particle filters is a simple, yet powerful and fully automatic multiple object tracking system.