Research On Key Technologies Of Target Tracking Based On Mean Shift And Particle Filter

Target tracking Video Image Sequence is one of the key research subjects in computer vision field and now is extensively used in Security guard, intelligent video surveillance, human movement analysis, intelligent traffic management, Man-machine interface interaction and military robots vision and so on. Target tracking analyzes and processes the moving target in video image sequence by Pattern recognition and image processing technologies so as to find the interested target location, and then ultimately get the parameter of the motion target, including target centroid, velocity and trajectory. These provide a source of data for video motion and scene analysis, thus achieving behavior understanding towards moving object so as to complete a higher-level task. There are many interference factors in videos, including complicated background environment, target posture change, target frequent occlusions and cross, illumination and climate change, etc. All these make target tracking a difficulty in computer vision research field. Though domestic and foreign scholars had extensive and deep research in this field, as well as solutions, many key problems haven’t effective treatment. All these urgently need mature and steady tracking technologies and methods.This dissertation studies key technologies in particle filter and mean shift, which is paid more attention by researchers in video image sequence tracking. Regard solving the defects of mean shift and particle filter algorithms as the main line and treating occlusions in complicated backgrounds as well as multi-features integration as the auxiliary line. The paper focuses on resolving modeling method in mean shift, degradations and multi-targets tracking in particle filtering target tracking, etc. The main tasks in this thesis are:(1) Multi-features integration on mean shift target trackingMethod of targets modeling with space corrected background weighted histogram is provided. Mean shift tracking is designed based on space corrected background weighted histogram. Firstly, this thesis offers the detailed derivation and proof procedure of the new algorithm, as well as how to describe the targe appearance with space related background weighted histogram. Secondly, this thesis proposes the dynamic updating methods of background model to improve the tracking accuracy when background environment have great changes, so that tracking will less depends on target initial position. Then, feature integration is introduced into mean shift tracking to further solve tracking failure or false when there is similar color of target and background or blocking matters. Finally, testing, analysis and contrast is running in actual video sequence data so as to verify the robustness of tracking algorithm. Comparing with traditional mean shift and spatiogram target tracking, experiments shows that the mentioned algorithm achieves good results in the case of inaccurate initial target positioning and severe occlusions with long-term and colors similar to being tracked targets.(2) Multi-features integration particle filter target tracking optimized by Mean ShiftFocusing on degradation issue of particle filter, particles are effectively spreaded through introducing mean shift algorithm. All kinds of optimized mechanism is integrated in the two algorithms to achieve effective scattering and gathering for particles, so as to reduce particle sets and improve the calculating and sampling efficiency of particle filtering. In the period of particle dissemination, positions and directions of each particle is optimized by using sizes and scales adaptive mean shift, to initially solve the degradation issues. Uncertainty weight adaptive adjustable method makes particle adaptive updating weight. Effective methods of multi-feature integration are combined with weight updating so as to better descripe target appearance model, and effectively solve future degradation issues. In order to adapt to complicated background environment, algorithms combined with the corresponding model updating measures. Algorithm is test on different tracking video data which have similar background, similar background occlusions as well as large scale change. The experiment shows that comparing with proposed algorithm and existing particle filtering method, degradation issue is well solved as well as achieving significant improvement.(3) Mixture particle filtering multi-targets tracking based on multi-features integrationFocus on the problems how to continuously maintain multi-model of target distribution in particle filtering multi-target tracking, as well as how to control the multi-model increasing, this thesis designed mixture particle filter of non-parameterized recursive model. It can effectively maintain and treat multi-model issues. Inherent multi-model is maintained when standard particle filtering is ineffective and many difficulties are effectively solved in non-constraints tracking applications. Firstly, the mixture particle filtering process is recursive realized by Monte Carlo derivation. Correlation between particles is achieved by mixture weight algorithms. Secondly, in the process of tracking algorithm, it constructs mixture observation likelihood function by mixing the dynamic model of multi-features integration and adaboost detecting information. Suggestion density integrated adaboost can rapidly detect the targets, and particle filtering process can maintain single target effectively tracking. Meanwhile multi-features integration can effectively estimate the samples when target appearance has greatly changes. Thirdly, in order to conquer model drifting phenomenon, this thesis proposes methods of switching probability principal component analysis to update model, so that the target can steadily track when backgrounds are complicated and changeable. Test towards algorithms in rigidity and non-rigidity and change quantity multi-targets video sequences, and proofs that algorithms achieves effectively track towards determinate number or changeable number multi-targets.(4) Multi-target tracking on multi-features integration particle filteringFocus on data association and estimation problems in particle filtering multi-targets tracking, this thesis makes researches the multi-targets in the framework of particle filtering and Gibbs sampling. Firstly, it extended classical particle filtering into multi-targets state estimation in the given several observation process. Then data association is considered from randomness point of view. Gibbs sampling is regarded as the methods of estimation and distribution correlation vector, that is, Joint posterior distribution of target state is expressed through particle set. Target state vector and association probability was jointly estimated without list, trim, threshold and other algorithms. This avoids merger drawbacks. Test is running in bearings-only target and real video sequence. Experiments show that algorithms have strong ability of solving data association problems. There are satisfactory performance even in the circumstance of dense clutter interference and nonlinearity.