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Online learning of dynamic multi-view gallery for person Re-identification

机译：在线学习动态多视图画廊以重新识别人

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摘要

Person re-identification receives increasing attentions in computer vision due to its potential applications in video surveillance. In order to alleviate wrong matches caused by misalignment or missing features among cameras, we propose to learn a multi-view gallery of frequently appearing objects in a relatively closed environment. The gallery contains appearance models of these objects from different cameras and viewpoints. The strength of the learned appearance models lies in that they are invariant to viewpoint and illumination changes. To automatically estimate the number of frequently appearing objects in the environment and update their appearance models online, we propose a dynamic gallery learning algorithm. We specifically build up two datasets to validate the effectiveness of our approach in realistic scenarios. Comparisons with benchmark methods demonstrate promising performance in accuracy and efficiency of re-identification.
机译：由于人员重新识别在视频监控中的潜在应用，因此在计算机视觉中受到越来越多的关注。为了缓解由于相机之间未对准或缺少功能而导致的错误匹配，我们建议在相对封闭的环境中学习经常出现的物体的多视图画廊。图库包含来自不同相机和视点的这些对象的外观模型。学习的外观模型的优势在于它们对于视点和照明变化是不变的。为了自动估计环境中经常出现的物体的数量并在线更新其外观模型，我们提出了一种动态画廊学习算法。我们专门建立了两个数据集，以验证我们的方法在实际场景中的有效性。与基准方法的比较表明，重新识别的准确性和效率令人鼓舞。

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