颜色特征和超像素特征融合的人体目标再识别

在非重叠视域的多摄像机监控系统中，人体目标再识别有着重要的应用。针对再识别过程中面临的光照变化、视角变化、姿态变化、遮挡等问题，提出了融合全局颜色特征和超像素特征的方法，对颜色特征和超像素特征分配不同的权重，进行人体目标间的相似性度量。超像素特征是将前景图像分割成多个超像素，采用密集采样SIFT特征结合单词包(Bag-of-Words)框架对每个超像素进行描述。将得到的超像素特征和全局颜色特征结合建立人体目标模型，分别使用EMD(Earth Mover’s Distance)距离和巴氏距离度量目标间的相似性。对多个数据库进行实验，结果证明，该算法具有较高的识别率。 

Person Re-identification Based on Color Features and Superpixels Features

Person re-identification is one of the key issues in a non-overlapping multi-camera surveillance system. The method of person re-identification must deal with several challenges such as variations of illumination conditions, poses and occlusions. To seek for more robust features, the unsupervised training method that combining the global color features with the superpixels features was proposed. Specifically, the color feature and the superpixels feature were asssigned different weighted values. To obtain the superpixels feature of a human target, the foreground picture of a person should be divided into different patches using the superpixels segmentation’s method. Then, dense SIFT features and Bag-of-Words model were applied to describe superpixels. At last, superpixels features and global color features were combined to represent a person, and EMD (Earth Mover’s Distance) distance and Bhattacharyya distance were used to determine the similarity between the targets. Extensive experiments results show that the proposed method has a higher accuracy rate.