Research On Moving Target Tracking Algorithm On Dynamic Scene

Video-based target tracking image sequences is a hotspot of computer vision research field, themain task of it is detecting from image sequences, identifying and tracking moving targets evenbeing able to understand and describe the action goals. And it has a wide range of application invideo surveillance, human motion recognition, video retrieval, robot navigation, medical diagnosticsand virtual reality. Illumination changes in the natural scene, similar to the color of the backgroundand objectives, goals and objectives of the deformation occlusion is the main difficulty of themoving target tracking. For these influent factors，the papers study the moving target trackingalgorithms of the dynamics of natural scenes from the characteristics of the target extraction, featureupdates, feature fusion and tracking algorithms improvement.Firstly, the paper introduces two classic tracking algorithms: Mean Shift tracking and particlefilter tracking algorithm, as well as improved continuous adaptive mean shift (CamShift) trackingalgorithm based on the mean shift tracking, and analyzes their problems.In order to solve the natural scene illumination changes, background color similar goals andobjectives with partial occlusion, we propose a CamShift tracking method based on gray predictionmodel. In this method, the gray forecasting model is to predict the target location as CamShifttracking algorithm to track the initial search center, then CamShift new target position trackingalgorithm updates historical data as the next frame gray prediction model, the loop execution. It alsoproposes moving target color histogram choose temper model update strategy.For the presence of particles of degraded traditional particle filter tracking algorithms whenresampling, Not a good solution to partial occlusion morph targets and target tracking problems,This paper introduces the multi-feature fusion based particle swarm optimized particle filter trackingmethod, the Particle swarm optimization algorithm uses to promote the true state of the particlesampling area moving slowing particle degradation, improves the tracking performance of theparticle filter tracking algorithm. For target deformation and occlusion, this article introduces theNormalized moment of inertia (NMI) feature, and with color features multiplicative fusion strategyfusion is used to describe the target characteristics.Paper-based windows operating system experiments, using Visual Studio2008andOpenCV2.3.1as the programming platform; Experiments using standard test video sequences, theparameters for comparison for target tracking algorithm uses the parameters provided in the original literatures, Experiments show that tracking method based on gray prediction model CamShift hasbetter robustness in illumination changes, similar to the background color, and target partialocclusion case; based on multi-feature fusion Particle swarm optimized particle filter trackingmethod for dynamic background scene goals deformation, with better accuracy and robustness underpartial occlusion.