Visual Object Tracking Using Color and Texture Features Based on Mean-shift and Particle-kalman Filter Algorithm

作     者：Tan William Choa（蔡松燃） 

作者单位：华南理工大学 

学位级别：硕士

导师姓名：李彬

授予年度：2017年

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

摘      要：Due to increasing demand of video surveillance system,intelligent video surveillance system has become challenging subject in the field of computer vision *** intelligent video surveillance system there are four key steps,*** detection,object classification,object tracking,and object *** these steps,object tracking is considered to be a key and important task in intelligent video surveillance *** tracking is considered as difficult task because of several problems such as illumination variation,tracking non-rigid object,non-linear motion,occlusion,and requirement of real-time *** single algorithm in visual object tracking always has advantages and *** with only a single algorithm is therefore generally considered to be inadequate and inefficient because each individual algorithm has its *** Mean Shift tracking algorithm always suffers from large position errors,which may lead to the failure on tracking target in complex *** handle this problem,an improved Mean-shift Particle-Kalman Filter tracking algorithm based on texture and color features is *** the proposed method,a color feature based Mean-shift is used as the main tracking algorithm when the target-object is moving in linear motion or when there is no occlusion ***,when occlusion occurs or when the target-object is moving in non-linear motion,color and texture features fusion based Particle-Kalman Filter tracking algorithm is used as main tracking *** experiment results show that the proposed method can be implemented in single object tracking and able to cope with several tracking problems such as illumination variation,non-rigid deformation,non-linear movement,similar color interference,and ***,the experiment results show that the proposed tracking algorithm which utilizes multi features（color and texture features）to represent the target model has better perf