Study On Moving Object Detection And Tracking

Aiming at automatically understanding and analyzing the visual signals captured by various acquisition devices, computer vision has become one of the most advanced computer technologies and received a lot of attention in recent years. As an important subject of computer vision, object detection and tracking, an interdisciplinary field crossing computer science, artificial intelligence, optics and mathematics etc, has been widely utilized in a large number of practical cases, such as military visual missile guidance, industrial product detection and human-machine interface.The main goal of this thesis is to present some novel technologies to alleviate the key issues on object detection and tracking, and the elaboration of each technique is given as follows:1. To exactly track the interesting object in visible image, a bayesian classification based real-time object tracking method is proposed in this paper. Specially, the kalman filter has been explored to boost the tracking performance on computational complexity and localization precision. In terms of the constantly change related to the object scale, a dual-threshold mechanism based on the statistical analysis over the candidate object window was proposed to guarantee the adaptive adjustment of the tracking window. The model parameters have been also updated simultaneously according to the change of object and background in the practical scene. The experimental results on several video sequences show good real-time capacity and robustness of the proposed scheme.2. To alleviate the small samples size and high-dimension described problem in object tracking, an efficient method based on GLRAM(Generalized low rank matrix approximation) and PPCA(Probabilistic principal component analysis) has been presented in this thesis. First, the GLRAM has been utilized on the high-dimension features of training samples to obtain a compact representation, then, the PPCA model has been constructed based on them to seek the optimal object location of the candidate objects with the maximum PPCA model probability output in subsequent frames. In addition, taking account into robustness of the proposed technique to the change of the object, the PPCA model was updated dynamically in each frame.3. Aiming at alleviating the effect of noise on the performance of dim small object detection in infrared image sequences, a spatial-temporal based detection algorithm has been proposed by the utilization of the multi-view object information. The dim small object can be obtained by finding the connected component of these pixels with high confidence generated by frame difference and edge detection. Moreover, in order to improve the detection accuracy, the detection was combined with tracking process using validation approach based on feedback mechanism in which edge density and appearance information have been used to verify the accuracy of the results of tracking. This method reduced fall-out ratio and miss rate effectively.