联合增强局部最大发生特征和k-KISSME度量学习的行人再识别

Joint enhanced local maximal occurrence representation and k-KISSME metric learning for person re-identification

行人再识别是一种在监控视频中自动搜索行人的重要技术,该技术包含特征表示和度量学习2部分。有效的特征表示应对光线和视角变化具有鲁棒性,具有判别性的度量学习能够提高行人图像的匹配精度。但是,现有的特征大多都是基于局部特征表示或者全局特征表示,没有很好的集成行人外观的精细细节和整体外观信息且度量学习通常是在线性特征空间进行,不能高效地利用特征空间中的非线性结构。针对该问题,设计了一种增强局部最大发生的有效特征表示(eLOMO)方法,可以实现行人图像精细细节和整体外观信息的融合,满足人类视觉识别机制;并提出一种被核化的KISSME度量学习(k-KISSME)方法,其计算简单、高效,只需要对2个逆协方差矩阵进行估计。此外,为了处理光线和视角变化,应用了Retinex变换和尺度不变纹理描述符。实验表明该方法具有丰富和完整的行人特征表示能力,与现有主流方法相比提高了行人再识别的识别率。

Person re-identification is an important technique for automatically searching for pedestrians in surveillance videos. This technology consists of two key parts, feature representation and metric learning. Effective feature representations should be robust to changes in illumination and viewpoint, and the discriminative metric learning can improve the matching accuracy of person images. However, most of the existing features were based on local or global feature representation and failed to efficiently use the fine details and profile information of the appearance of pedestrians. More importantly, metric learning was usually conducted in a linear feature space, and nonlinear structures in the feature space couldn’t be efficiently utilized. To solve these problems, we first designed an effective feature representation called enhanced local maximal occurrence representation(eLOMO), which could realize the fusion of fine details and profile information of the appearance of the person image and satisfy the human visual recognition mechanism. Furthermore, we proposed a kernelized KISSME metric learning(k-KISSME) method, simple and efficient, only requiring two inverse covariance matrices to be estimated. In addition, to handle changes in light and viewing angle, we applied Retinex transforms and scale-invariant texture descriptors. Experiments show that the proposed method possesses the ability regarding abundant and integral person feature representation and improves the recognition rate of person re-identification in comparison with the existing mainstream methods.