Vehicle Re-Identification in Aerial Imagery: Dataset and Approach

In this work, we construct a large-scale dataset for vehicle re-identification (ReID), which contains 137k images of 13k vehicle instances captured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based vehicle ReID dataset. To increase intra-class variation, each vehicle is captured by at least two UAVs at different locations, with diverse view-angles and flight-altitudes. We manually label a variety of vehicle attributes, including vehicle type, color, skylight, bumper, spare tire and luggage rack. Furthermore, for each vehicle image, the annotator is also required to mark the discriminative parts that helps them to distinguish this particular vehicle from others. Besides the dataset, we also design a specific vehicle ReID algorithm to make full use of the rich annotation information. It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforming the evaluated baselines and state-of-the-art vehicle ReID approaches.

[1]  Longhui Wei,et al.  Person Transfer GAN to Bridge Domain Gap for Person Re-identification , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[2]  Junwei Han,et al.  Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Tao Xiang,et al.  Multi-level Factorisation Net for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Silvio Savarese,et al.  Monocular Multiview Object Tracking with 3D Aspect Parts , 2014, ECCV.

[5]  Wu Liu,et al.  Large-scale vehicle re-identification in urban surveillance videos , 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).

[6]  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).

[7]  Lucas Beyer,et al.  In Defense of the Triplet Loss for Person Re-Identification , 2017, ArXiv.

[8]  Wei Wu,et al.  Distractor-aware Siamese Networks for Visual Object Tracking , 2018, ECCV.

[9]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Ming-Hsuan Yang,et al.  Learning Spatial-Aware Regressions for Visual Tracking , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Xiaogang Wang,et al.  Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Devendra Patil,et al.  Eye in the Sky: Real-Time Drone Surveillance System (DSS) for Violent Individuals Identification Using ScatterNet Hybrid Deep Learning Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[13]  Xiaogang Wang,et al.  Person Re-identification with Deep Similarity-Guided Graph Neural Network , 2018, ECCV.

[14]  Samuel Murray,et al.  Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[15]  Bernard Ghanem,et al.  A Benchmark and Simulator for UAV Tracking , 2016, ECCV.

[16]  Yee Wei Law,et al.  UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition , 2018, ECCV Workshops.

[17]  Bernard Ghanem,et al.  W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[19]  Larry S. Davis,et al.  Jointly Optimizing 3D Model Fitting and Fine-Grained Classification , 2014, ECCV.

[20]  Michael Felsberg,et al.  Unveiling the Power of Deep Tracking , 2018, ECCV.

[21]  Shuicheng Yan,et al.  End-to-End Comparative Attention Networks for Person Re-Identification , 2016, IEEE Transactions on Image Processing.

[22]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Tieniu Tan,et al.  Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles , 2012, IEEE Transactions on Image Processing.

[24]  Ngai-Man Cheung,et al.  Efficient and Deep Person Re-identification Using Multi-level Similarity , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  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).

[26]  Xiaogang Wang,et al.  Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[28]  Jing Xu,et al.  Attention-Aware Compositional Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[29]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Qinghua Hu,et al.  Vision Meets Drones: A Challenge , 2018, ArXiv.

[31]  N. Dinesh Reddy,et al.  CarFusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[32]  Xiong Chen,et al.  Learning Discriminative Features with Multiple Granularities for Person Re-Identification , 2018, ACM Multimedia.

[33]  Shiliang Zhang,et al.  RAM: A Region-Aware Deep Model for Vehicle Re-Identification , 2018, 2018 IEEE International Conference on Multimedia and Expo (ICME).

[34]  Larry S. Davis,et al.  AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video , 2011, AVSS.

[35]  Slawomir Bak,et al.  Domain Adaptation through Synthesis for Unsupervised Person Re-identification , 2018, ECCV.

[36]  Supun Samarasekera,et al.  Vehicle tracking across nonoverlapping cameras using joint kinematic and appearance features , 2011, CVPR 2011.

[37]  Muhittin Gokmen,et al.  Human Semantic Parsing for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[38]  Rongrong Ji,et al.  Cross-Modality Person Re-Identification with Generative Adversarial Training , 2018, IJCAI.

[39]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[40]  Bingbing Ni,et al.  Scale-Transferrable Object Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[41]  Xiaogang Wang,et al.  FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification , 2018, NeurIPS.

[42]  Jiebo Luo,et al.  DOTA: A Large-Scale Dataset for Object Detection in Aerial Images , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[43]  Xiaoou Tang,et al.  A large-scale car dataset for fine-grained categorization and verification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Rynson W. H. Lau,et al.  CREST: Convolutional Residual Learning for Visual Tracking , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[45]  Larry S. Davis,et al.  Learning Rich Features for Image Manipulation Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[46]  Shaogang Gong,et al.  Harmonious Attention Network for Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[47]  Gang Wang,et al.  Person Re-identification with Cascaded Pairwise Convolutions , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.