Reidentification of Persons Using Clothing Features in Real-Life Video
Reidentification of Persons Using Clothing Features in Real-Life Video
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DOI：
10.1155/2017/5834846
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Guodong Zhang;Peilin Jiang;Kazuyuki Matsumoto;Minoru Yoshida;K. Kita
中科院分区：
文献类型：
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作者：
Guodong Zhang;Peilin Jiang;Kazuyuki Matsumoto;Minoru Yoshida;K. Kita
人物重新识别是自动视频处理中的一项基本任务，其目标是在不重叠的摄像机上跟踪人物。由于照明、姿势和相机属性的差异，从不同的非重叠相机观察移动的人时，他们的外观往往不同。颜色直方图是物体的全局特征，可用于识别。此直方图描述对象上所有颜色的分布。然而，使用颜色直方图有两个缺点。首先，在不同的光线和不同的角度下，颜色会发生不同的变化。其次，传统的颜色直方图缺乏空间信息。我们使用一个基于感知的颜色空间来解决传统直方图的光照问题。我们还使用了空间金字塔匹配（SPM）模型，以改善图像的空间信息的颜色直方图。最后，利用高斯混合模型（GMM）的主要颜色特征更能适应场景变化，提高了在不同场景下不同颜色空间下检索结果的稳定性，从而实现了对人脸的再识别。通过一系列的实验，我们发现了影响人的再识别的不同特征之间的关系。
Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing. Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties. The color histogram is a global feature of an object that can be used for identification. This histogram describes the distribution of all colors on the object. However, the use of color histograms has two disadvantages. First, colors change differently under different lighting and at different angles. Second, traditional color histograms lack spatial information. We used a perception-based color space to solve the illumination problem of traditional histograms. We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms. Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes. Through a series of experiments, we found the relationships of different features that impact person reidentification.