Region-aware multi-resolution learning for vehicle re-identification using mask

Abstract. As an instance-level recognition problem, the key to effective vehicle re-identification (Re-ID) is to carefully reason the discriminative and viewpoint-invariant features of vehicle parts at high-level and low-level semantics. However, learning part-based features requires a laborious human annotation of some factors as attributes. To address this issue, we propose a region-aware multi-resolution (RAMR) Re-ID framework that can extract features from a series of local regions without extra manual annotations. Technically, the proposed method improves the discriminative ability of the local features through parallel high-to-low resolution convolutions. We also introduce a position attention module to focus on the prominent regions that can provide effective information. Given that the vehicle Re-ID performance can be affected by background clutters, we use the image obtained through foreground segmentation to extract local features. Results show that using original and foreground images can enhance the Re-ID performance compared with using either the original or foreground images alone. In other words, the original and foreground images complement each other in the vehicle Re-ID process. Finally, we aggregate the global appearance and local features to improve the system performance. Extensive experiments on two publicly available vehicle Re-ID datasets, namely, VeRi-776 and VehicleID, are conducted to validate the effectiveness of each proposed strategy. The findings indicate that the RAMR model achieves significant improvement in comparison with other state-of-the-art methods.

[1]  Francois Bremond,et al.  Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification , 2019, CVPR Workshops.

[2]  Andrea Cavallaro,et al.  Omni-Scale Feature Learning for Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[3]  Bing He,et al.  Part-Regularized Near-Duplicate Vehicle Re-Identification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Rama Chellappa,et al.  A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[5]  Jitendra Malik,et al.  Hypercolumns for object segmentation and fine-grained localization , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Xinyu Zhang,et al.  Part-Guided Attention Learning for Vehicle Instance Retrieval , 2019, IEEE Transactions on Intelligent Transportation Systems.

[7]  Xiu-Shen Wei,et al.  Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification , 2018, ACCV.

[8]  Meng Yang,et al.  Large-Margin Softmax Loss for Convolutional Neural Networks , 2016, ICML.

[9]  Jian Cheng,et al.  Additive Margin Softmax for Face Verification , 2018, IEEE Signal Processing Letters.

[10]  Shengcai Liao,et al.  Vehicle Re-Identification Using Quadruple Directional Deep Learning Features , 2018, IEEE Transactions on Intelligent Transportation Systems.

[11]  Shuo Wang,et al.  PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[12]  Tao Mei,et al.  PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance , 2018, IEEE Transactions on Multimedia.

[13]  Jun Fu,et al.  Dual Attention Network for Scene Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[15]  Ling-Yu Duan,et al.  VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Tao Mei,et al.  A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance , 2016, ECCV.

[17]  Wei Jiang,et al.  Multi-Domain Learning and Identity Mining for Vehicle Re-Identification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[18]  Bingbing Ni,et al.  Pose Transferrable Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Xing Ji,et al.  CosFace: Large Margin Cosine Loss for Deep Face Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Tiejun Huang,et al.  Deep Relative Distance Learning: Tell the Difference between Similar Vehicles , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Xiaogang Wang,et al.  Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[22]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[23]  Giorgos Tolias,et al.  Fine-Tuning CNN Image Retrieval with No Human Annotation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[25]  Ling-Yu Duan,et al.  Group-Sensitive Triplet Embedding for Vehicle Reidentification , 2018, IEEE Transactions on Multimedia.

[26]  Liang Wang,et al.  Mask-Guided Contrastive Attention Model for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  Yongjian Hu,et al.  Variational Representation Learning for Vehicle Re-Identification , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[28]  Yinghuan Shi,et al.  A Mask Based Deep Ranking Neural Network for Person Retrieval , 2019, 2019 IEEE International Conference on Multimedia and Expo (ICME).

[29]  Zhangyang Wang,et al.  In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[30]  Liang Zheng,et al.  Circle Loss: A Unified Perspective of Pair Similarity Optimization , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Stefanos Zafeiriou,et al.  ArcFace: Additive Angular Margin Loss for Deep Face Recognition , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Shengyong Chen,et al.  Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval , 2020, IEEE Transactions on Intelligent Transportation Systems.

[33]  Liang Zheng,et al.  Re-ranking Person Re-identification with k-Reciprocal Encoding , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Zijun Zhang,et al.  Improved Adam Optimizer for Deep Neural Networks , 2018, 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS).

[35]  Abhir Bhalerao,et al.  Person reidentification using deep foreground appearance modeling , 2018, J. Electronic Imaging.

[36]  Yang Yang,et al.  ABD-Net: Attentive but Diverse Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[37]  Yan Wang,et al.  Resource Aware Person Re-identification Across Multiple Resolutions , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[38]  Dong Liu,et al.  Deep High-Resolution Representation Learning for Human Pose Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).