Research On Visual Tracking Algorithm Based On Particle Filter

Airborne electro-optical stabilizing and tracking system dependent on detection, search and infrared aiming has become one of keypoint of electro-optical equipments and has very important significance to national defence, social stabilization and production. In the tracking system, image tracking is a key technology and directly determines the performance of the tracking system. Meanwhile, image tracking is an active topic in computer vision field and has wide applications in surveillance, human-machine interaction and robot navigation, etc.Image tracking methods are roughly divided into two main categories: probabilistic tracking and deterministic tracking. Probabilistic tracking methods have beome dominant due to their stable and robust performance. Kalman filter and particle filer are their two classical representatives. Kalman filter has rigorous limitation to system models and posterior distribution and thus is merely capable of dealing with linear, Gaussian and mono-modal situations. However, in image tracking application, the posterior density is often non-linear, non-Gaussian and multi-modal and thus the application of Kalman filter is limited. Different from Kalman filter, Particle filter is greatly developed in tracking field because of its ability of maintaining multi-modal distribution of the state and robustness to noise. However, the conventional particle filter based tracking algorithm has some defciencies, such as high computational cost and low sampling efficiency. In addition, the complexity of tracking scenes poses great challengs on tracking algorithms.To improve the robustness of particle filter based tracking algorithms and futher provide theory and algorithm supports for science research and engineering applications, the thesis proposes some enhanced tracking algorithms which mainly improve the tracking performance from two aspects. One is to design effective sampling methods, cluster the particles around the target as possible as, thus better representing the posterior density and improving the sampling efficency and decreasing the computational load. The other is to design better discriminant and accurate likelihood models including the two cue fusion based likelihood model, the adaptive likelihood model and hybrid likelihood model. Then these likelihood models are embedded into particle filter framework for tracking. The research contents of the thesis are as follows:Firstly, to overcome the shortcoming of single visual cue in complex enviroments, a tracking algirhtm based on adaptive cue fusion mechanism is proposed. The color cue and shape cue are ulitlized to represent the target and democratic integration is applied to fusion these two cues, thus facilating the tracking algorithm on-line adjusting the weight of two cues and utilizing their strongpoints. Due to the use of reliable cues for tracking, the failure of single cue in complex scenes is solved. During designing particle filter based tracking algorithm, the likelihood model is constructed dependent on adaptive cue fusion mechanism, thus enhancing the robustness of tracking algorithm. The tracking results demonstrate that the tracking algorithm based on adaptive cue fusion is able to successfully track target in presence of move, rotation and partial occlusion. When the target with similar color appears, the tracking algorithm with multi-part color model can distinguish the target, thus addressing the collision problem of similar targets.Secondly, to deal with low sampling efficency and huge computational load, two enhanced particle filter based tracking algorithms are proposed: auxiliary kernel particle filter based tracking algorithm and hierarchical sample tracking algorithm. Firstly, auxiliary particle filter is applied for sampling particles and then mean shift is invoked to move particles to their local maximum of the posterior density. Compared with particle filter and kernel particle filter, auxiliary kernel particle filter takes recent observations into account, thereby alleviating the incapability of completely covering around the true target in particle filter and kernel particle filter and decreasing the computational cost. Hierarchical sample tracking algorithm considers not only the importance of particles but also the diversity, thus being capable of better representing the posterior density. The performance of hierarchical sample tracking algorithm is superior to that of particle filter and kernel particle filter when the tracked target moves suddenly. The experiments show two tracking algorithms proposed can better track targets with sudden move, rotation, occlusions in complex enviroments.Thirdly, to reflect the changes of target appearance during tracking and improve the robustness and reliabity of tracking algorithms in dynamic scenes, an adaptive appearance model based tracking algorithm is proposed. Incremental kernel density approximate is used to on-line update grey appearance model, thus facilating real-time processing. During implementing tracking algorithm, the likelihood model of particle filter is construted by adaptive appearance model and furthermore occlusion handling strategy is invoked to declare outlier pixels and deal with occlusion events, thus decreasing their influence on the appearance model. Numours experiments demonstrate that compared with the fixed appearance model based tracking algoirhtm, the tracking algoirhtm based on adaptive appearance model achieves more better tracking performance in presence of lighting changes, pose variations, expression changes, partial occlusions and even full occlusions.Fourthly, to improve the ability of distinguishing the target and background, a hybrid appearance model based tracking algorithm is developed. The hybrid appearance models are composed of a fixed appearance model, a fast change appearance model and an eigenbasis appearance model. Weighted incremental principal component analysis is invoked to learn and update the eigenbasis of object appearance, thus effectively increasing the ability of discriminating the object. The hybrid appearance model is further applied to design the likelihood model, thereby making the tracking algorithm more stable and accuate under complex environments. In addition, occlusion handing strategy is also used to provent the eigenbais from erroneously absorbing the appearance of occluding object. Experimental results show the propsed tracking algorithm is superior to the tracking algoirhtm dependent on single appearance model under the variations of lighting and pose, partial and even full occlusions.The purpose of the thesis mainly focuses on the application of tracking single target. However, due to the wide applications of image tracking in computer vision field, the research of the thesis has important significance to the related application of computer vision.