计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 180-186.doi: 10.11896/jsjkx.191100061
李灏, 唐敏, 林建武, 赵云波
LI Hao, TANG Min, LIN Jian-wu, ZHAO Yun-bo
摘要： 为了提升跨模态行人重识别算法的识别精度,提出了一种基于改进困难三元组损失的特征学习框架。首先,改进了传统困难三元组损失,使其转换为全局三元组损失。其次,基于跨模态行人重识别中存在模态间变化及模态内变化的问题,设计了模态间三元组损失及模态内三元组损失,以配合全局三元组损失进行模型训练。在改进困难三元组损失的基础上,首次在跨模态行人重识别模型中设计属性特征来提高模型的特征提取能力。最后,针对跨模态行人重识别中出现的类别失衡问题,首次将Focal Loss用于替代传统交叉熵损失来进行模型训练。相比现有算法,在RegDB数据集实验中,所提框架在各项指标中均有1.9%~6.4%的提升。另外,通过消融实验证明了3种方法均能提升模型的特征提取能力。
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| [1]SONG W R,ZHAO Q Q,CHEN C H,et al.Survey on pedestrian re-identification research[J].CAAI transactions on intelligent systems,2017,12(6):770-780. [2]ZHENG L,YANG Y,HAUPTMANN A G.Person re-identification:Past,present and future[J].arXiv:1610.02984,2016. [3]MATSUKAWA T,SUZUKI E.Person re-identification usingCNN features learned from combination of attributes[C]//2016 23rd International Conference on Pattern Recognition (ICPR).Cancun:IEEE Press,2016:2428-2433. [4]SCHROFF F,KALENICHENKO D,PHILBIN J.Facenet:A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE Press,2015:815-823. [5]YE M,LAN X,LI J,et al.Hierarchical discriminative learning for visible thermal person re-identification[C]//Thirty-Second AAAI Conference on Artificial Intelligence.New Orleans AAAI Press,2018. [6]NGUYEN D,HONG H,KIM K,et al.Person recognition system based on a combination of body images from visible light and thermal cameras[J].Sensors,2017,17(3):605. [7]HAO Y,WANG N,LI J,et al.HSME:Hypersphere ManifoldEmbedding for Visible Thermal Person Re-Identification[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Hawaii:AAAI Press,2019,33:8385-8392. [8]WANG G,ZHANG T,CHENG J,et al.RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment[C]//Proceedings of the IEEE International Conference on Computer Vision.Seoul:IEEE Press,2019:3623-3632. [9]YU Q,CHANG X,SONG Y Z,et al.The devil is in the middle:Exploiting mid-level representations for cross-domain instance matching[J].arXiv:1711.08106,2017. [10]HERMANS A,BEYER L,LEIBE B.In defense of the tripletloss for person re-identification[J].arXiv:1703.07737,2017. [11]LIN Y,ZHENG L,ZHENG Z,et al.Improving person re-identification by attribute and identity learning[J].Pattern Recognition,2019,95:151-161. [12]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision.Venice:IEEE Press,2017:2980-2988. [13]BEN X Y,XU S,WANG K J.Review on Pedestrian Gait Feature Expression and Recognition[J].Pattern Recognition and Artificial Intelligence,2012,25(1):71-81. [14]BAO Z M,GONG S R,ZHONG S,et al.Person Re-identification Algorithm Based on Bidirectional KNN Ranking Optimization[J].Computer Science,2019,46 (11):267-271. [15]WU A,ZHENG W S,LAI J H.Robust depth-based person re-identification[J].IEEE Transactions on Image Processing,2017,26(6):2588-2603. [16]YE M,WANG Z,LAN X,et al.Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking[C]//the 27th International Joint Conference on Artificial Intelligence.Stockholm:Morgan Kaufmann Press,2018:1092-1099. [17]WU A,ZHENG W S,YU H X,et al.Rgb-infrared cross-modality person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision.Venice:IEEE Press,2017:5380-5389. [18]LIN L,WANG G,ZUO W,et al.Cross-domain visual matching via generalized similarity measure and feature learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,39(6):1089-1102. [19]LIAO S,LI S Z.Efficient psd constrained asymmetric metriclearning for person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision.Santiago:IEEE Press,2015:3685-3693. [20]LIAO S,HU Y,ZHU X,et al.Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Santiago:IEEE Press,2015:2197-2206. [21]YE M,WANG Z,LAN X,et al.Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking[C]//the 27th International Joint Conference on Artificial Intelligence.Stockholm:Morgan Kaufmann Press,2018:1092-1099. [22]WANG Z,WANG Z,ZHENG Y,et al.Learning to reduce dual-level discrepancy for infrared-visible person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE Press,2019:618-626. [23]SELVARAJU R R,COFSWELL M,DAS A,et al.Grad-cam:Visual explanations from deep networks via gradient-based localization[C]//Proceedings of the IEEE International Confe-rence on Computer Vision.Venice:IEEE Press,2017:618-626. |
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