光电工程
光電工程
광전공정
OPTO-ELECTRONIC ENGINEERING
2015年
2期
52-58
,共7页
视觉跟踪%PCA子空间%增量式子空间学习%粒子滤波
視覺跟蹤%PCA子空間%增量式子空間學習%粒子濾波
시각근종%PCA자공간%증량식자공간학습%입자려파
visual tracking%PCA subspace%incremental subspace learning%particle filtering
针对当前目标跟踪算法鲁棒性低且运算慢的问题,本文提出了一种基于子空间学习的实时目标跟踪算法。该方法在粒子滤波跟踪框架下,采用增量式PCA子空间学习方法学习一个正交子空间,利用学习到的正交子空间对目标外观进行线性表示;针对目标在遮挡、运动模糊等复杂干扰状态下容易产生跟踪漂移的问题,本文建立了一个将遮挡等复杂因素考虑在内的观测模型和模板更新方案,解决了基于最小均方误差准则的传统观测模型在复杂场景下的跟踪漂移问题。实验结果表明,本文的跟踪方法能够达到很高的跟踪精度,同时也达到了接近实时的跟踪速度。
針對噹前目標跟蹤算法魯棒性低且運算慢的問題,本文提齣瞭一種基于子空間學習的實時目標跟蹤算法。該方法在粒子濾波跟蹤框架下,採用增量式PCA子空間學習方法學習一箇正交子空間,利用學習到的正交子空間對目標外觀進行線性錶示;針對目標在遮擋、運動模糊等複雜榦擾狀態下容易產生跟蹤漂移的問題,本文建立瞭一箇將遮擋等複雜因素攷慮在內的觀測模型和模闆更新方案,解決瞭基于最小均方誤差準則的傳統觀測模型在複雜場景下的跟蹤漂移問題。實驗結果錶明,本文的跟蹤方法能夠達到很高的跟蹤精度,同時也達到瞭接近實時的跟蹤速度。
침대당전목표근종산법로봉성저차운산만적문제,본문제출료일충기우자공간학습적실시목표근종산법。해방법재입자려파근종광가하,채용증량식PCA자공간학습방법학습일개정교자공간,이용학습도적정교자공간대목표외관진행선성표시;침대목표재차당、운동모호등복잡간우상태하용역산생근종표이적문제,본문건립료일개장차당등복잡인소고필재내적관측모형화모판경신방안,해결료기우최소균방오차준칙적전통관측모형재복잡장경하적근종표이문제。실험결과표명,본문적근종방법능구체도흔고적근종정도,동시야체도료접근실시적근종속도。
Because of the poor efficiency and effectiveness of current visual tracking algorithms, a real-time object tracking algorithm is proposed based on subspace learning.Under the framework of particle filtering, this paper uses the incremental PCA subspace method to learn an orthogonal subspace, and then get the linear representation of target appearance. In order to avoid the tracking drift produced by complicated interference, such as occlusions, motion blur and so on, an observation model and a template update scheme are built, which consider the complicated interference especially occlusions, to solve the drift problem of the traditional observation model based onminimum mean square error. The experimental results show that the algorithm in complicated conditions can be well implemented compared with several state-of-the-art algorithms.