摘要
针对表观发生剧烈变化时的目标跟踪问题,提出一种新的基于自适应分块表观模型的视觉目标跟踪算法.将目标表观描述为一组具有内在空间上几何结构关系约束的局部图像块,在跟踪过程中通过自动添加和删除局部图像块适应目标的表观变化,同时利用全局颜色属性值确定新的图像块的位置,克服了传统分块算法不能及时更新表观模型的局限性.实验结果表明,所提出算法对表观变化具有较高的自适应性,在表观发生剧烈变化时可实现准确的目标跟踪.
For the tracking problem when the target undergoes rapid and significant appearance changes, a novel tracking algorithm is presented. The object＇s appearance is represented by a set of local patches with inherent spatial geometric constraints relationship. It probabilistically adapts to the object＇s appearance changes by removing and adding the local patches. The locations of new patches are determined by the global color property, which can improve the limitations of the traditional patch-based algorithms that the appearance model can＇t be updated in time during tracking. Experimental results show that the proposed algorithm performs in many cases with high adaptivity to appearance changes, which has high accuracy to objects with drastically changes.
出处
《控制与决策》
EI
CSCD
北大核心
2016年第3期448-452,共5页
Control and Decision
基金
国家自然科学基金项目(61375079)
关键词
视觉跟踪
局部图像块
表观变化
visual tracking
local patches
appearance changes
