基于特征融合的行人重识别方法

针对行人重识别中已有方法难以解决行人图像光照、视角变化大的问题，提出了一种基于特征融合的行人重识别方法。首先利用Retinex变换对图像进行预处理；然后将CN特征与原有的颜色和纹理特征融合，并通过区域和块划分的方式提取直方图获得图像特征；最后采用不同的距离学习方法在4个数据集上进行行人重识别。实验结果表明，融合后的特征对行人图像具有更好的表述能力，实现了重识别精度的较大提升，验证了方法的有效性。

Person re-identification based on feature fusion

Existing methods in person re-identification cannot describe the pedestrian well for significant intra-class variations in illumination and viewpoint. In order to solve this problem, a method based on feature fusion is proposed. Firstly, the image is pre-processed using the Rentiex algorithm. Then the CN feature is combined with the color and texture features. Histograms are extracted through regional and block partition, and then the feature of image is obtained. Finally, person re-identification is performed on four public datasets with different metric learning methods. The experimental results show that the feature combined with CN achieves a stronger representation and increases the accuracy obviously.