Research On Target Tracking Based On Particle Filter

Target tracking technology has being a research focus in computer vision andimage processing, which has important application value in intelligence monitoring,vision navigation, intelligent transportation, human-computer interaction, defensereconnaissance, and is one of the key technology of weapon systems. Although manyscholars of the past two decades are in depth study of target tracking technology, butbecause of obtaining the inaccurate target template in the initial stage of tracking,complexity movement of the target in the image plane, changing of the targetobservational characteristics, complex background interference, occlusion and otherfactors, the current target tracking technology still can not meet the need of militaryand civilian areas, and therefore target tracking technology still need to be studied indepth.Target tracking problem can be defined as when having the priori information oftarget, after obtaining visual information on new observation, the iterative process ofobtaining the posterior probability of the target state vector, so the target trackingproblem can be modeled as Bayesian estimation. This thesis is mainly based onparticle filter tracking framework, focusing on the dynamic model and observationmodel; simultaneously researching on target detection and target segmentationalgorithm, trying to integrating target detection and segmentation algorithm intracking algorithm based on particle filter to reduce tracking error and improve tracking precision. The main innovation and research results are as follows:1. Dynamic model of particle filter is to describe how the target moving, and thedynamic model describes quite different from the pattern of target actual movement,will inevitably leading the particles cannot accurately coverage of the target trueposition in predicting stage, and leading tracking error gradually accumulate, evenleading tracking failure. For tracking ground target with cameras in airborneenvironment, this thesis proposes a Two-Stage Acceleration dynamic model(TSA),which includes liberal model and conservative model. In the liberal model, targetspeed will be modeled as a zero mean Gaussian Markov process, and can describe themotion pattern between RW model and NCV model through parameter adjustment.Conservative model estimate the current velocity of the target, and alternate theaverage target speed of Gauss-Markov process in liberal model. Experimental resultsshow that the TSA model can predict the target motion accurately when the targetmoving in large scale in the image plane.2. Observation model of particle filter determines the weight of the particles andaffect the tracking precision directly. Classic observational features are color, contourand so on. Kernel density estimation histogram feature is often used and the keytechnology is the selecting of kernel function. In this thesis, asymmetric kernelfunction is constructed based on the Snake contour extraction algorithm, whichintegrated the color information of the target, and then the kernel density estimationhistogram of target is constructed as observation model of particle filter. The modelnot only can describe the target appearance information better, but also can be updatedin real-time when observation features of target changes. Addition, this model isconstructed in real-time algorithm and has practical engineering value.3. For complex background around the target when tracking, the asymmetricalgorithm kernel is constructed with larger error. To deal with such problem, thisthesis will integrate particle filter tracking algorithm with GrabCut imagesegmentation algorithm. First more robust multi-directional GrabCut algorithm isproposed, and then MGC-PF particle filter algorithm is proposed. Experimental results show that this algorithm have better tracking precision when tracking target incomplex background.4. To design a segmentation algorithm based on GrabCut which can integratetracking algorithm better, make full use of the time correlation and spatial correlationin target tracking process, this thesis will integrate random forest classifier to GrabCutsegmentation algorithm, RF-GC segmentation algorithm is proposed, and finallyRF-GC-PF particle filter tracking algorithm is proposed. Experimental results showthat the algorithm can solve the problem of poor tracking precision due to thecombination factors of complex movement of target, observation features changingand the complex background.This thesis attempts to solve the problem of poor tracking precision due tocomplex movement of target, target observation features changing in real-time, thecomplex background and other factors, and the development trend of integratingimage segmentation, target detection algorithm to traditional tracking algorithm isresearched, analysis and outlook.