Object tracking based on visual attention
Object tracking based on visual attention
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DOI：
10.1109/icinfa.2016.7832119
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发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Mingqiang Lin;Houde Dai
中科院分区：
文献类型：
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作者：
Mingqiang Lin;Houde Dai
人类有能力快速确定外部视觉刺激的优先级，并在一个场景中定位他们最感兴趣的地方。受此机制的启发，我们提出了一种基于视觉注意的鲁棒目标跟踪算法。在显著性图的指导下，融合运动特征和颜色特征对目标状态进行估计。基于背景模板生成的密集外观模型，采用主成分分析法计算显著性特征。运动特征提取方法采用贝叶斯决策规则对背景和前景进行分类。大量实验表明，该方法在处理光照变化、姿态变化、遮挡和背景杂波等情况时，能够很好地对抗当前最先进的跟踪方法。
Humans have the capability to quickly prioritize external visual stimuli and localize their most interest in a scene. Inspired by this mechanism, we propose a robust object tracking algorithm based on visual attention. We fuse motion feature and color feature to estimate the target state under the guidance of saliency map. Principal Component Analysis method is used to compute saliency feature based on the dense appearance model generated from the background templates. Motion feature is extracted by using the method which is a Bayesian decision rule for classification of background and foreground. Numerous experiments demonstrate the proposed method performs well against state-of-the-art tracking methods when dealing with illumination change, pose variation, occlusion, and background clutter situations.