Study On Visual Object Tracking And Its Application

This thesis mainly focuses on the problem of visual object tracking, which is a key problem of intelligent video analysis that is demanded by many applications in computer vision, such as intelligent surveillance, human-computer interfaces, robotics and multimedia. Robust long-term visual tracking in unconstrained environment is still very challenging due to the real-world complications such as clutters, appearance change, low image quality, and frame-cut. Based on the analysis of research actuality of object tracking which contains strong spatial-temporal relevance and the theory and method of image signal processing, pattern recognition and online machine learning, we propose several robust real-time object tracking algorithms, involving single target tracking and multiple target tracking, and apply them to address other problems in computer vision. The main contributions of this thesis are given as follows:(1) In order to improve the robustness of tracking algorithm using random ferns for detection, we propose an enhanced random ferns which is integrated into our tracking framework as the object detector. Its main idea is to exploit the potential distribution properties of feature vectors which are here called hidden classes by on-line clustering of feature space for each leaf-node of ferns. The kernel density estimation technique is then used to evaluate unlabeled samples based on the hidden classes which are set as the data points of the kernel function. Experimental results demonstrate the effectiveness and the improved robustness of our approach.(2) To address the problem of improving the ability of adaptation to the variation of target and meanwhile ensuring the accuracy of online learning for tracking system, we propose a method of active context learning for object tracking. The approach exploits both target and background information on the fly automatically and builds the structural constraint by using the active context learning to enhance the adaptability for variation of the target and stability of tracking. An optical-flow-based motion region extraction method is integrated into the context learning framework to address the problem of fast target motion or abrupt camera motion. Experimental results demonstrate the improved tracking performance of our tracker.(3) Existing Hough-based tracking systems have not achieved real-time performance. To deal with this problem, we propose a Hough ferns based method for real-time object tracking. In the tracking-by-detection framework, Hough ferns, which are based on random ferns, sample the local appearances of object as training set, and compute and save the Hough votes for each leaf-node. Hough ferns and object model are leaned on-line to adapt to the variation of object. Experimental results validate the effectiveness and robustness of our tracker which can run in real time.(4) In order to improve the capability of object recognition of the detector and then the robustness of the tracking system, we propose a method of online learning multiple detectors for object tracking. The method uses the random ferns as the basic detector. The entire and the local appearances of the target and the connected objects which are explored by the context learning are used synchronously as the training data to build and upgrade the object detector on-line. Thus it is able to detect the objects with different classes independently. Since different detection is related to different object class, the results of object detections are fused as the measurements and the probabilities of configuration hypotheses for the measurements to the target are calculated to find the target location for visual tracking task. Experimental results validate the effectiveness and robustness of our approach and demonstrate its better tracking performance than several state-of-the-art methods.(5) To reduce the computational complexity of the algorithm achieving real-time multiple target tracking, we propose a collaboration model in which the acceleration difference between two targets is used to calculate the motion correlation value based on the two-dimensional Gaussian function. By the collaboration model, the location of occluded target is estimated using the motion information from other targets. The proposed approach is computationally efficient and robust. Experimental results exhibit the performance of our tracker based on our approach.(6) For the application of object tracking, the methods proposed can be applied to the corresponding scenarios. In particular, in order to address the problem of detecting and locating the anterior cruciate ligament of human’s knee in medical image and promote the study of its reconstruction operation, we proposes a hierarchical detection based method to locate the anterior cruciate ligament. The location task is considered to be to perform the global and the local detections successively. The features are selected according to the type of image samples, and the corresponding global and local detectors are built based on the random forests respectively to first find the entire region of the anterior cruciate ligament and then recognize its definite area. Experimental results based on the real MRI images validate the effectiveness and accuracy of our method.