利用混合双通路神经网络的跨模态行人重识别

Cross-modality person re-identification utilizing the hybrid two-stream neural networks

因为红外图像数据可以有效地弥补单一可见光数据在低照度条件下的不足,因此研究跨模态可见光—红外行人重识别将为构建一个全天候、全场景智能视频监控系统提供强有力的技术支撑。跨模态行人重识别的核心问题是构建多模态数据之间的统一共享特征表达,关键在于有效区分跨模态数据中模态共享/特有的特征信息。基于此,提出了一种基于混合双通路神经网络的跨模态行人重识别方法。该方法深入地分析了混合双通路神经网络中模态共享参数层和模态特有非共享参数层对跨模态行人重识别模型的影响,同时充分利用了不同模态数据类内特征分布的一致性约束和类间相关性系数在不同模态间的一致性约束来提升模型精度。针对所设计的整体神经网络架构,该方法采用了混合自适应学习率调整的模型训练策略来提升模型的特征学习能力。最后,通过大量实验分析验证了所提方法的有效性,并且该方法在当前可见光—红外图像行人重识别两个标准数据集(SYSU-MM01和RegDB)上都取得了领先当前主流方法的识别精度。

An infrared image can effectively make up for the shortcomings of single-modality visible-light image data under low illumination conditions.Therefore,the study of cross-modality Visible-to-Infrared person re-identification will provide a strong technical support for constructing an intelligence video surveillance system under various lighting conditions.The key for cross-modality person re-identification is to construct a unified shared feature representation among multi-modal data,which needs to effectively distinguish the modal-shared/modal-specific feature information in the cross-modal data.Based on this,this paper proposes a cross-modality person re-identification method based on a hybrid dual-channel neural network.This method deeply analyzes the influence of the parameter-shared layer and the non-shared parameter layer on the cross-modality person re-identification model in the hybrid dual-channel neural network architecture.Besides cross-entropy loss,we also use the intra-class distribution and inter-class correlation constraints in the loss function to further improve the re-identification performance.In the optimization process,we effectively utilize the adaptive learning rate adjustment strategy to improve the feature learning capability of the neural network architecture.Experimental results illustrate the effectiveness of the proposed method.Also,we obtain a superior performance on the two widely used cross-modality person re-identification benchmark datasets,SYSU-MM01 and RegDB.