Robust visual tracking using autoregressive hidden Markov Model
Robust visual tracking using autoregressive hidden Markov Model
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DOI：
10.1109/cvpr.2012.6247898
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Dong Woo Park;Junseok Kwon;Kyoung Mu Lee
中科院分区：
文献类型：
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作者：
Dong Woo Park;Junseok Kwon;Kyoung Mu Lee
最近关于视觉跟踪的研究表明，通过处理目标对象的外观变化，精确度有了显著的提高。尽管大多数研究提出了提取目标的时间不变特征并自适应地更新外观模型的方案，但本论文集中在对连续的目标外观之间的概率依赖进行建模(图1-(A))。为了实现这一目的，在自回归隐马尔可夫模型(AR-HMM)下建立了一种新的贝叶斯跟踪框架，其中隐含了序列目标外观之间的概率依赖关系。在每个时间步长的学习阶段，该跟踪器根据目标样本的视觉相似性将其分成几个簇，并将簇特定的分类器学习为多个外观模型，每个外观模型代表特定类型的目标外观。然后学习这些外观模型之间的依赖关系。在搜索阶段，考虑到目标状态对以前使用的外观模型的依赖关系，通过推断最可能的外观模型来估计目标状态。该方法在12个具有挑战性的视频序列上进行了测试，结果表明，该方法在准确率上优于目前最先进的方法。
Recent studies on visual tracking have shown significant improvement in accuracy by handling the appearance variations of the target object. Whereas most studies present schemes to extract the time-invariant characteristics of the target and adaptively update the appearance model, the present paper concentrates on modeling the probabilistic dependency between sequential target appearances (Fig. 1-(a)). To actualize this interest, a new Bayesian tracking framework is formulated under the autoregressive Hidden Markov Model (AR-HMM), where the probabilistic dependency between sequential target appearances is implied. During the learning phase at each time step, the proposed tracker separates formerly seen target samples into several clusters based on their visual similarity, and learns cluster-specific classifiers as multiple appearance models, each of which represents a certain type of the target appearance. Then the dependency between these appearance models is learned. During the searching phase, the target state is estimated by inferring the most probable appearance model under the consideration of its dependency on formerly utilized appearance models. The proposed method is tested on 12 challenging video sequences containing targets with abrupt appearance variations, and demonstrates that it outperforms current state-of-the-art methods in accuracy.