基于置信区域内多级动态层表达的类贯序蒙特卡洛视觉跟踪方法

Quasi-Sequential Monte Carlo Visual Tracking Based on Multilevel Dynamic Layer Representations in Confidence Region

视觉跟踪是智能监控、机器人和视觉导航等领域的核心技术.针对现有类贯序蒙特卡洛跟踪方法复杂度高、实时性差的问题,本文提出了一种融合置信区域内多级动态层表达的跟踪方法,采用更加可靠、有效的粒子模拟状态后验概率.该方法利用检测模块得到目标可能出现的置信区域,根据真实目标尺寸给出一种粒子采样策略,每个粒子代表一级动态层表达,并为每个粒子建立双层运动模型;构建Mean-Shift分块观测模型以引入空间和外观信息,同时根据子块的匹配程度计算粒子权值、检测目标遮挡状态并提出模型更新策略.在公开视频序列上同经典粒子滤波和Mean-Shift等算法的实验对比结果证明了本文算法具有较优的跟踪准确度和实时性.

Visual tracking is a core technology for the application domains of intelligent monitoring,robotics and vi-sion navigation,etc.Aiming at the problem of high complexity and poor real-time performance in the existing quasi-sequen-tial Monte Carlo tracking algorithms,this paper presents a method based on multilevel dynamic layer representations,which simulates the posteriori probability of a state using more reliable and effective particles.Then a sampling strategy is proposed in confidence areas derived from the detector,in which each particle represents a dynamic representation and has a two-layer motion model.The observation model based on parted-mean-shift is constructed for space and appearance information.De-pending on the degree of matching sub-blocks,the weight of particles is calculated and a way to detect the occlusion state of an object is put forward for realtime model update.Experimental results using challenging public video sequences show bet-ter accuracy and efficiency of the proposed method,compared with the classical particle filter and mean-shift algorithms,etc.