Multi-cues object tracking based on motion consistence in random field

Chen Chenshu; Zhang Jun; Xie Zhao; Gao Jun

doi:10.11834/jig.20150107

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Multi-cues object tracking based on motion consistence in random field
Vol. 20, Issue 1, Pages: 59-71(2015)
Published Online：31 December 2014，

Published：2015
DOI： 10.11834/jig.20150107
稿件说明：

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DOI：

Chen Chenshu, Zhang Jun, Xie Zhao, Gao Jun. Multi-cues object tracking based on motion consistence in random field[J]. Journal of Image and Graphics, 2015, 20(1): 59-71. DOI： 10.11834/jig.20150107.

摘要

通过建立各线索间的关联

提高多线索目标跟踪方法的鲁棒性

利用简单而有效的模型使多线索目标跟踪方法的表达和实现变得容易。在不同线索描述下的目标对象间引入运动一致性约束

利用链状结构随机场模型表达不同线索描述下的目标对象及其约束关系

将多线索目标跟踪问题转化为随机场目标函数的简单优化求解。实验中结合亮度直方图、方向梯度直方图和局部二进制模式描述目标对象。15组公测视频序列上的实验结果表明

所提方法相对于多种优秀的目标跟踪方法

在目标受到遮挡、运动模糊、光照变化、背景杂乱等因素干扰时

获得了较低中心位置误差和较高的精度值

反映了所提方法的有效性。运动一致性约束能够较好地增强各线索间的关联

通过链状结构的随机场模型表达该约束关系和各线索描述下的目标对象

在提高跟踪鲁棒性的同时

使跟踪方法的实现变得简单。

Abstract

The relationships among different cues are established to improve the robustness of a tracking method.A simple but effective model is utilized to easily implement the tracking method. A motion-consistency constraint is proposed among objects represented by different cues.A chain-structure Markov random field is used to express the objects represented by different cues and the constraint among them. The tracking problem is converted into a simple optimization of the target function of a Markov random field. The cues used in the experiment are luminance histogram

oriented gradient histogram

and local binary pattern. The comparison between several state-of-the-art tracking methods and the proposed method on 15 video sequences shows the effectiveness of the latter.The proposed method has low position error and high tracking accuracy when an object is influenced by occlusion

motion blur

illumination changes

and clutter. A motion-consistency constraint enhances the relationships among different cues to a certain degree. Expressing the constraint and the objects represented by different cues through a chain-structure Markov random field improves the robustness of the tracking method and makes it easy to implement.