红外与激光工程
紅外與激光工程
홍외여격광공정
INFRARED AND LASER ENGINEERING
2011年
7期
1387-1396
,共10页
多目标视觉跟踪%有限t分布混合模型%混合t分布粒子滤波器%序列蒙特卡洛方法%混合粒子滤波器%Boosted粒子滤波器
多目標視覺跟蹤%有限t分佈混閤模型%混閤t分佈粒子濾波器%序列矇特卡洛方法%混閤粒子濾波器%Boosted粒子濾波器
다목표시각근종%유한t분포혼합모형%혼합t분포입자려파기%서렬몽특잡락방법%혼합입자려파기%Boosted입자려파기
multi-object visual tracking%finite mixture model of t distribution%mixtures of t distribution particle filters%sequential Monte Carlo methods%mixtures of particle filters%Boosted particle filter
由于目标数量的变化,观测数据的岐义性和目标间的遮挡,多目标视觉跟踪问题面临多种困难.基于目标分布的有限t分布混合模型提出了一种混合t分布粒子滤波器以实现多目标跟踪.在算法中,每个被跟踪目标指派一个独立的粒子滤波器,显式处理当新目标出现在场景中时对应粒子滤波器的初始化,当被跟踪目标消失时,对应粒子滤波器的删除.混合t分布粒子滤波器算法不仅能够跟踪多种类型的多目标,还能够持续跟踪遮挡消除之后的多目标.为了展现混合t分布粒子滤波器的跟踪性能,完成了基于颜色分布的跟踪多种不同颜色和相同颜色的多目标实验,对比了混合t分布粒子滤波器,混合粒子滤波器以及Boosted粒子滤波器的跟踪性能.实验结果表明:文中算法不仅能够跟踪数量可变的多目标,进行实时计算,而且具有更好的鲁棒性.
由于目標數量的變化,觀測數據的岐義性和目標間的遮擋,多目標視覺跟蹤問題麵臨多種睏難.基于目標分佈的有限t分佈混閤模型提齣瞭一種混閤t分佈粒子濾波器以實現多目標跟蹤.在算法中,每箇被跟蹤目標指派一箇獨立的粒子濾波器,顯式處理噹新目標齣現在場景中時對應粒子濾波器的初始化,噹被跟蹤目標消失時,對應粒子濾波器的刪除.混閤t分佈粒子濾波器算法不僅能夠跟蹤多種類型的多目標,還能夠持續跟蹤遮擋消除之後的多目標.為瞭展現混閤t分佈粒子濾波器的跟蹤性能,完成瞭基于顏色分佈的跟蹤多種不同顏色和相同顏色的多目標實驗,對比瞭混閤t分佈粒子濾波器,混閤粒子濾波器以及Boosted粒子濾波器的跟蹤性能.實驗結果錶明:文中算法不僅能夠跟蹤數量可變的多目標,進行實時計算,而且具有更好的魯棒性.
유우목표수량적변화,관측수거적기의성화목표간적차당,다목표시각근종문제면림다충곤난.기우목표분포적유한t분포혼합모형제출료일충혼합t분포입자려파기이실현다목표근종.재산법중,매개피근종목표지파일개독립적입자려파기,현식처리당신목표출현재장경중시대응입자려파기적초시화,당피근종목표소실시,대응입자려파기적산제.혼합t분포입자려파기산법불부능구근종다충류형적다목표,환능구지속근종차당소제지후적다목표.위료전현혼합t분포입자려파기적근종성능,완성료기우안색분포적근종다충불동안색화상동안색적다목표실험,대비료혼합t분포입자려파기,혼합입자려파기이급Boosted입자려파기적근종성능.실험결과표명:문중산법불부능구근종수량가변적다목표,진행실시계산,이차구유경호적로봉성.
Abstract:Multiple objects visual track exhibits a number of difficulties due to the variable number of objects,the ambiguity of the observations and the presence of partial occlusions.Since the representing target distributed as finite t distribution mixture models,a mixtures of t distribution particle filters (MTPF) was introduced to track multiple objects.In this paper,each tracked object was assigned a separate particle filter to handle the instantiation and removal of filters when new objects entered the scene or previously tracked objects were removed.The proposed filter can not only track multiple objects of different types,but also continue to track objects through occlusion situations.To present the tracking performance of the MTPF algorithm,experiments were performed using color-based tracking of multiple objects with different and identical colors,and comparison experiments between mixtures of t distribution particle filters,mixtures of particle filters and the boosted particle filters were conducted.Experimental results demonstrate the proposed tracking algorithm not only track a variable number of targets with the real-time computation,but also perform the more robustness.