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Joint Detection and Identification Feature Learning for Person Search
Open accessAbstract
Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world scenarios where the annotations of pedestrian bounding boxes are unavailable and the target person needs to be searched from a gallery of whole scene images. To close the gap, we propose a new deep learning framework for person search. Instead of breaking it down into two separate tasks-pedestrian detection and person re-identification, we jointly handle both aspects in a single convolutional neural network. An Online Instance Matching (OIM) loss function is proposed to train the network effectively, which is scalable to datasets with numerous identities. To validate our approach, we collect and annotate a large-scale benchmark dataset for person search. It contains 18,184 images, 8,432 identities, and 96,143 pedestrian bounding boxes. Experiments show that our framework outperforms other separate approaches, and the proposed OIM loss function converges much faster and better than the conventional Softmax loss.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Scalable Person Re-identification: A Benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.133 · Cited by 4,506
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Van Gool, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_2 · Cited by 3,958
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_2 · Cited by 2,914
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
Li, W., Zhao, R., Xiao, T., & Wang, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.27 · Cited by 2,611
Self-citationPerson re-identification by Local Maximal Occurrence representation and metric learning
Liao, S., Hu, Y., Zhu, X., & Li, S. Z. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298832 · Cited by 2,091
Self-citationUnlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro
Zheng, Z., Zheng, L., & Yang, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.405 · Cited by 1,984
Fast Feature Pyramids for Object Detection
Dollar, P., Appel, R., Belongie, S., & Perona, P. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2300479 · Cited by 1,955
Large scale metric learning from equivalence constraints
Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., & Bischof, H. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247939 · Cited by 1,606
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
Gray, D., & Tao, H. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_21 · Cited by 1,466
An improved deep learning architecture for person re-identification
Ahmed, E., Jones, M., & Marks, T. K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299016 · Cited by 1,248
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
Cheng, D., Gong, Y., Zhou, S., Wang, J., & Zheng, N. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.149 · Cited by 1,247
Unsupervised Salience Learning for Person Re-identification
Zhao, R., Ouyang, W., & Wang, X. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.460 · Cited by 1,025
Self-citationEvaluating Appearance Models for Recognition, Reacquisition, and Tracking
Gray, D. A., Brennan, S., & Hai, T. (2007) · Cited by 984
Person Re-identification by Descriptive and Discriminative Classification
Hirzer, M., Beleznai, C., Roth, P. M., & Bischof, H. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21227-7_9 · Cited by 961
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
Xiao, T., Li, H., Ouyang, W., & Wang, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.140 · Cited by 958
Self-citationIs Faster R-CNN Doing Well for Pedestrian Detection?
Zhang, L., Lin, L., Liang, X., & He, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_28 · Cited by 852
Self-citationPerson Re-identification in the Wild
Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., & Tian, Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.357 · Cited by 830
Intelligent multi-camera video surveillance: A review
Wang, X. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.07.005 · Cited by 694
Self-citationDeep feature learning with relative distance comparison for person re-identification
Ding, S., Lin, L., Wang, G., & Chao, H. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.005 · Cited by 684
Self-citationPerson Re-Identification by Support Vector Ranking
Prosser, B., Zheng, W.-S., Gong, S., & Xiang, T. (2010) · Procedings of the British Machine Vision Conference 2010 · doi:10.5244/c.24.21 · Cited by 657
Locally Aligned Feature Transforms across Views
Li, W., & Wang, X. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.461 · Cited by 574
Self-citationPerson Reidentification Using Spatiotemporal Appearance
Gheissari, N., Sebastian, T. B., & Hartley, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.223 · Cited by 552
Shape and Appearance Context Modeling
Wang, X., Doretto, G., Sebastian, T., Rittscher, J., & Tu, P. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4409019 · Cited by 481
Self-citationZheng, W.-S., Gong, S., & Xiang, T. (2009) · Procedings of the British Machine Vision Conference 2009 · doi:10.5244/c.23.23 · Cited by 444
Pedestrian detection aided by deep learning semantic tasks
Tian, Y., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299143 · Cited by 407
Self-citationFiltered channel features for pedestrian detection
Zhang, S., Benenson, R., & Schiele, B. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298784 · Cited by 395
Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification
Liao, S., & Li, S. Z. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.420 · Cited by 395
Self-citationLearning to rank in person re-identification with metric ensembles
Paisitkriangkrai, S., Shen, C., & van den Hengel, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298794 · Cited by 385
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
Cai, Z., Saberian, M., & Vasconcelos, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.384 · Cited by 326
Convolutional Channel Features
Yang, B., Yan, J., Lei, Z., & Li, S. Z. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.18 · Cited by 301
Self-citationPartial Person Re-Identification
Zheng, W.-S., Li, X., Xiang, T., Liao, S., Lai, J., & Gong, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.531 · Cited by 295
Taking a deeper look at pedestrians
Hosang, J., Omran, M., Benenson, R., & Schiele, B. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299034 · Cited by 285
Structured Feature Learning for Pose Estimation
Chu, X., Ouyang, W., Li, H., & Wang, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.510 · Cited by 261
Self-citationHamdoun, O., Moutarde, F., Stanciulescu, B., & Steux, B. (2008) · 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras · doi:10.1109/icdsc.2008.4635689 · Cited by 237
Person Re-Identification with Correspondence Structure Learning
Shen, Y., Lin, W., Yan, J., Xu, M., Wu, J., & Wang, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.366 · Cited by 163
Multi-Scale Learning for Low-Resolution Person Re-Identification
Li, X., Zheng, W., Wang, X., Xiang, T., & Gong, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.429 · Cited by 163
Self-citationPerson Search in a Scene by Jointly Modeling People Commonness and Person Uniqueness
Xu, Y., Ma, B., Huang, R., & Lin, L. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654965 · Cited by 147
Self-citationKeeping Track of Humans: Have I Seen This Person Before?
Zajdel, W., Živković, Z., & Kröse, B. (2006) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2005.1570420 · Cited by 126
Yu, S.-I., Yang, Y., & Hauptmann, A. G. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.476 · Cited by 67
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Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
Zhang, Y., Wang, C., Wang, X., Zeng, W., & Liu, W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01513-4 · Cited by 1,861
Self-citationTowards Real-Time Multi-Object Tracking
Wang, Z., Zheng, L., Liu, Y., Li, Y., & Wang, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_7 · Cited by 1,195
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Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., & Tian, Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.357 · Cited by 830
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Zhong, Z., Zheng, L., Luo, Z., Li, S., & Yang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00069 · Cited by 685
Self-citation
Metadata
- Authors
- Xiao, T., Li, S., Wang, B., Lin, L., & Wang, X.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3376-3385
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963574614
- Cited by
- 890
- References
- 46
- FWCI
- 27.59
- Type
- conference paper
- Language
- en
- Open access
- green
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