中文摘要：

行人再识别中的难点在于在不同摄像机中同一行人的图像差异较大,单一特征难以稳定地描述图像,而采用多种特征融合时无法准确分配权重。针对这一缺陷,本文提出了多核支持向量机多示例学习的行人再识别算法。首先提取行人在A、B摄像机下二张图片的分块HSV颜色特征和分块SIFT局部特征并构建词袋,将二者作为示例样本封装成包;其次对多核支持向量机模型进行了优化,采用高斯核和多项式核线性融合对包进行训练,并用多示例学习获得最优权重;最后本文算法在VIPe R标准数据集上进行了测试,识别准确率通过计算十次实验的平均准确度来获得,并用CMC曲线进行表示,同时也对样本的匹配结果进行排序。实验结果表明本文算法与多个优秀的算法相比,鲁棒性和识别准确度都获得了提高。

英文摘要：

The difficulty of person re-identification is that the same person images in different cameras are significantly different, which is difficult to stably describe the images by a single feature, while the fusion by a variety of features can＇t distribute their weights exactly. To solve the problems, a person re-identification algorithm based on multi-kernel support vector machine by multi-instance learning is proposed. Firstly, the blocked color features in HSV space and local features of SIFT from the same people image under different cameras are extracted, and the bag of words are constructed to SIFT features. Both of them are taken as two instances and encapsulated as a bag especially. Secondly, the multi-kernel support vector machine model is optimized, the bags are trained by the linear fusion kernel between Gaussian and polynomial, and then the optimal weighting ratio is obtained by multi-instance learning. Finally, this algorithm is tested on the VIPe R dataset, the accuracy rate of recognition is an average accuracy of ten times experiments, and expressed by CMC curves. At the same time, the matching result of the sample is also sorted. The experiments show that the robustness and recognition rate of this algorithm achieve the same and even better results while compared with several state of the art algorithms.