XiongweiWu Awesome Object Detection Save Abandoned

Resource collections of object detection

Project README

Awesome-Object-Detection

Last Update: 2019/08/16

TODO:

  • Fix the link and format issues
  • Add paper link to SOTA tables

A list of awesome object detection resources.

Recently we released survey (Recent Advances in Deep Learning for Object Detection) to the community. In this survey, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc. Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning.

After completing this survey, we decided to release the collected resource of object detection. We will keep updating our survey as well as this resource collection, since this area moves too fast. If you have any questions or suggestions, please feel free to contact us.

Table of Contents

Citing this work

If this repository is useful, please cite our survey.

@article{wu2019recent,
    title={Recent Advances in Deep Learning for Object Detection},
    author={Xiongwei Wu, Doyen Sahoo, Steven C.H. Hoi},
    journal={arXiv preprint arXiv:1908.03673},
    year={2019}
}

1. Generic Object Detection

1.1 Two-stage Detection

2014 CVPR

  1. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, R. Girshick, J. Donahue, T. Darrell, J. Malik, [OpenAccess],[Supplementary], [Caffe], RCNN

2014 ECCV

  1. Spatial pyramid pooling in deep convolutional networks for visual recognition, K. He, X. Zhang, S. Ren, J. Sun, [Arxiv], [Caffe-Matlab], SPP-Net

2015 CVPR

  1. Deepid-net: Deformable deep convolutional neural networks for object detection,W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, C.-C. Loy, [OpenAccess]
  2. segdeepm: Exploiting segmentation and context in deep neural networks for object detection, Y. Zhu, R. Urtasun, R. Salakhutdinov, S. Fidler, [OpenAccess]
  3. Deformable part models are convolutional neural networks, R. Girshick, F. Iandola, T. Darrell, J. Malik, [OpenAccess]

2015 ICCV

  1. Fast r-cnn, R. Girshick, [OpenAccess], [Caffe-Python], Fast R-CNN
  2. Object detection via a multi-region and semantic segmentation-aware cnn model, S. Gidaris, N. Komodakis, [OpenAccess], [Caffe], MR-CNN
  3. Deepproposal: Hunting objects by cascading deep convolutional layers, A. Ghodrati, A. Diba, M. Pedersoli, T. Tuytelaars, L. Van Gool, [OpenAccess], [MatConvnet], Deepproposal

2015 NeurIPS

  1. Faster r-cnn: Towards real-time object detection with region proposal networks, S. Ren, K. He, R. Girshick, J. Sun, [OpenAccess],[Arxiv],[Caffe-Matlab], [Caffe-Python],[Pytorch], [TensorFlow], [MXNet], Faster R-CNN

2016 CVPR

  1. Hypernet: Towards accurate region proposal generation and joint object detection, T. Kong, A. Yao, Y. Chen, F. Sun, [OpenAccess], HyperNet
  2. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks, S. Bell, C. Lawrence Zitnick, K. Bala, R. Girshick, [OpenAccess], ION
  3. Object detection from video tubelets with convolutional neural networks, K. Kang, W. Ouyang, H. Li, X. Wang, [OpenAccess], [Caffe], T-CNN
  4. Instance-aware semantic segmentation via multitask network cascades, J. Dai, K. He, J. Sun, [OpenAccess], [Caffe], MNC
  5. Adaptive object detection using adjacency and zoom prediction, Y. Lu, T. Javidi, S. Lazebnik, [Arxiv], [Caffe], AZ-Net
  6. Training region-based object detectors with online hard example mining, A. Shrivastava, A. Gupta, R. Girshick, [OpenAccess], [Caffe], OHEM
  7. Locnet: Improving localization accuracy for object detection, S. Gidaris, N. Komodakis, [OpenAccess], [Matlab], LocNet
  8. Craft objects from images, B. Yang, J. Yan, Z. Lei, S. Z. Li, [OpenAccess], [Caffe], CRAFT

2016 ECCV

  1. Contextual priming and feedback for faster r-cnn, A. Shrivastava, A. Gupta, [OpenAccess]
  2. Gated bi-directional cnn for object detection, X. Zeng, W. Ouyang, B. Yang, J. Yan, X. Wang, [OpenAccess]

2016 NeurIPS

  1. R-fcn: Object detection via region-based fully convolutional networks, J. Dai, Y. Li, K. He, J. Sun, [OpenAccess], [Caffe-Matlab], [Caffe-Python], R-FCN

2016 Others

  1. Beyond skip connections: Top-down modulation for object detection, A. Shrivastava, R. Sukthankar, J. Malik, A. Gupta, in: arXiv preprint arXiv:1612.06851, 2016. [Arxiv], TDM
  2. A multipath network for object detection, S. Zagoruyko, A. Lerer, T.-Y. Lin, P. O. Pinheiro, S. Gross, S. Chintala, P. Dollar, in: BMVC, 2016. [Arxiv], [Torch], MultiPathNet
  3. Pvanet: deep but lightweight neural networks for real-time object detection, K.-H. Kim, S. Hong, B. Roh, Y. Cheon, M. Park, in: arXiv preprint arXiv:1608.08021, 2016. [Arxiv], [Caffe], PVANet

2017 CVPR

  1. Feature pyramid networks for object detection, T.Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, [OpenAccess], [Caffe2], FPN
  2. Perceptual generative adversarial networks for small object detection, J. Li, X. Liang, Y. Wei, T. Xu, J. Feng, S. Yan, [OpenAccess], PGAN
  3. A-fast-rcnn: Hard positive generation via adversary for object detection, X. Wang, A. Shrivastava, A. Gupta, [OpenAccess], Caffe],A-Fast-RCNN
  4. Mimicking very efficient network for object detection, Q. Li, S. Jin, J. Yan, [OpenAccess]
  5. Learning non-maximum suppression, J. Hosang, R. Benenson, B. Schiele, [OpenAccess], [TensorFlow]
  6. Speed/accuracy trade-offs for modern convolutional object detectors, J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al., [OpenAccess], [TensorFlow]

2017 ICCV

  1. Mask R-CNN, K. He, G. Gkioxari, P. Dollar, R. Girshick, [OpenAccess],[Caffe2], [Slides], Mask R-CNN
  2. Denet: Scalable real-time object detection with directed sparse sampling, L. Tychsen-Smith, L. Petersson, [OpenAccess],[Theano], DeNet
  3. Deformable convolutional networks, J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, Y. Wei, [OpenAccess],[MXNet], DCN
  4. Couplenet: Coupling global structure with local parts for object detection, Y. Zhu, C. Zhao, J. Wang, X. Zhao, Y. Wu, H. Lu, [OpenAccess],[Caffe], CoupleNet
  5. Spatial memory for context reasoning in object detection, X. Chen, A. Gupta, [OpenAccess], SMN
  6. Soft-nms – improving object detection with one line of code, N. Bodla, B. Singh, R. Chellappa, L. S. Davis, [OpenAccess], [Caffe]

2017 Others

  1. Light-head rcnn: In defense of two-stage object detector, Z. Li, C. Peng, G. Yu, X. Zhang, Y. Deng, J. Sun, in: arXiv preprint arXiv:1711.07264, 2017. [Arxiv], [Pytorch], [TensorFlow]
  2. Zoom out-and-in network with recursive training for object proposal, H. Li, Y. Liu, W. Ouyang, X. Wang, in: arXiv preprint arXiv:1702.05711, 2017. [Arxiv]

2018 CVPR

  1. Cascade r-cnn: Delving into high quality object detection, Z. Cai, N. Vasconcelos, [OpenAccess], [Caffe], [Caffe2] Cascade R-CNN
  2. Detnet: A backbone network for object detection, Z. Li, C. Peng, G. Yu, X. Zhang, Y. Deng, J. Sun, [OpenAccess], [Pytorch*], DetNet
  3. An analysis of scale invariance in object detection–snip, B. Singh, L. S. Davis, [OpenAccess], [MXNet], SNIP
  4. Multi-scale location-aware kernel representation for object detection, H. Wang, Q. Wang, M. Gao, P. Li, W. Zuo, [OpenAccess], [Caffe], MLKR
  5. Feature selective networks for object detection, Y. Zhai, J. Fu, Y. Lu, H. Li, [OpenAccess]
  6. Pseudo mask augmented object detection, X. Zhao, S. Liang, Y. Wei, [OpenAccess]
  7. Structure inference net: Object detection using scene-level context and instance-level relationships, Y. Liu, R. Wang, S. Shan, X. Chen, [OpenAccess], [TensorFlow], SIN
  8. Relation networks for object detection, H. Hu, J. Gu, Z. Zhang, J. Dai, Y. Wei, [OpenAccess], [MXNet]
  9. Path Aggregation Network for Instance Segmentation, S. Liu, L. Qi, H. Qin, J. Shi and J. Jia, [OpenAccess], [Pytorch]

2018 ECCV

  1. Acquisition of localization confidence for accurate object detection, B. Jiang, R. Luo, J. Mao, T. Xiao, Y. Jiang, [OpenAccess], [Pytorch], IoU-Net
  2. Revisiting rcnn: On awakening the classification power of faster rcnn, B. Cheng, Y. Wei, H. Shi, R. Feris, J. Xiong, T. Huang, [OpenAccess], [MXNet]
  3. Learning region features for object detection, J. Gu, H. Hu, L. Wang, Y. Wei, J. Dai, [OpenAccess]
  4. Deep regionlets for object detection, H. Xu, X. Lv, X. Wang, Z. Ren, R. Chellappa, [OpenAccess]
  5. Context refinement for object detection, Z. Chen, S. Huang, D. Tao, [OpenAccess]

2018 NeurIPS

  1. Metaanchor: Learning to detect objects with customized anchors, T. Yang, X. Zhang, Z. Li, W. Zhang, J. Sun, [OpenAccess], MetaAnchor
  2. Sniper: Efficient multi-scale training, B. Singh, M. Najibi, L. S. Davis, [OpenAccess], [MXNet], SNIPER

2019 AAAI

  1. Derpn: Taking a further step toward more general object detection, L. J. Z. X. Lele Xie, Yuliang Liu, [OpenAccess], [Caffe], DeRPN
  2. Object Detection based on Region Decomposition and Assembly, S.-H Bae, [OpenAccess], R-DAD

2019 CVPR

  1. Mask scoring r-cnn, Z. Huang, L. Huang, Y. Gong, C. Huang, X. Wang, [OpenAccess], [Pytorch], Mask Scoring R-CNN
  2. Deformable convnets v2: More deformable, better results, S. L. Xizhou Zhu, Han Hu, J. Dai, [OpenAccess], [MXNet], DCNv2
  3. Grid r-cnn, X. Lu, B. Li, Y. Yue, Q. Li, J. Yan, [OpenAccess], [mmdetection]
  4. Nas-fpn: Learning scalable feature pyramid architecture for object detection, G. Ghiasi, T.-Y. Lin, Q. V. Le, [OpenAccess], [TensorFlow], NAS-FPN
  5. Bounding Box Regression with Uncertainty for Accurate Object Detection, Y. He, C. Zhu, J. Wang, M. Savvides, X. Zhang, [OpenAccess], [Caffe2], KL-Loss
  6. Libra R-CNN: Towards Balanced Learning for Object Detection, J. Pang, K. Chen, J. Shi, H. Feng, W. Ouyang, D. Lin, [OpenAccess], [Pytorch], [mmdetection], Libra R-CNN
  7. Region Proposal by Guided Anchoring, J. Wang, K. Chen, S. Yang, C. C. Loy, D. Lin, [OpenAccess], [mmdetection]

2019 ICCV

  1. Rethinking imagenet pre-training, R. G. Kaiming He, P. Dollro, [OpenAccess]

2019 Others

  1. Scale-aware trident networks for object detection, Y. Li, Y. Chen, N. Wang, Z. Zhang, in: arXiv preprint arXiv:1901.01892, 2019. [OpenAccess], [MXNet], TridentNet

2019 NeurIPS

1.2 One-stage Detection

Before 2014

  1. Overfeat: Integrated recognition, localization and detection using convolutional networks, P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun, in: arXiv preprint arXiv:1312.6229, 2013. [Arxiv], [Torch], Overfeat

2016 CVPR

  1. You only look once: Unified, real-time object detection, J. Redmon, S. Divvala, R. Girshick, A. Farhadi, [OpenAccess], [DarkNet], YOLO

2016 ECCV

  1. SSD: Single shot multibox detector, W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, A. C. Berg, [OpenAccess], [Caffe], SSD

2017 CVPR

  1. Yolo9000: better, faster, stronger, J. Redmon, A. Farhadi, [OpenAccess], [DarkNet], YOLOv2
  2. Ron: Reverse connection with objectness prior networks for object detection, T. Kong, F. Sun, A. Yao, H. Liu, M. Lu, Y. Chen, [OpenAccess], [Caffe], RON

2017 ICCV

  1. Focal loss for dense object detection, T.Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar, [OpenAccess], [Caffe2], RetinaNet
  2. Dsod: Learning deeply supervised object detectors from scratch, Z. Shen, Z. Liu, J. Li, Y.-G. Jiang, Y. Chen, X. Xue, [OpenAccess], [Caffe], DSOD

2017 Others

  1. Dssd: Deconvolutional single shot detector, C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, A. C. Berg, in: arXiv preprint arXiv:1701.06659, 2017. [OpenAccess], [Caffe], DSSD
  2. Residual features and unified prediction network for single stage detection, K. Lee, J. Choi, J. Jeong, N. Kwak, in: arXiv preprint arXiv:1707.05031, 2017. [OpenAccess]
  3. Enhancement of ssd by concatenating feature maps for object detection, J. Jeong, H. Park, N. Kwak, in: arXiv preprint arXiv:1705.09587, 2017. [OpenAccess]
  4. Fssd: Feature fusion single shot multibox detector, Z. Li, F. Zhou, in: arXiv preprint arXiv:1705.1712.00960, 2017. [OpenAccess], FSSD
  5. Learning object detectors from scratch with gated recurrent feature pyramids, Z. Shen, H. Shi, R. Feris, L. Cao, S. Yan, D. Liu, X. Wang, X. Xue, T. S. Huang, in: arXiv preprint arXiv:1712.00886, 2017. [OpenAccess], [Caffe]

2018 CVPR

  1. Single-shot refinement neural network for object detection, S. Zhang, L. Wen, X. Bian, Z. Lei, S. Z. Li, [OpenAccess], [Caffe], RefineDet
  2. Scale-transferrable object detection, P. Zhou, B. Ni, C. Geng, J. Hu, Y. Xu, [OpenAccess], [Pytorch], STDN
  3. Single-shot object detection with enriched semantics, Z. Zhang, S. Qiao, C. Xie, W. Shen, B. Wang, A. L. Yuille, [OpenAccess], [Caffe], DES

2018 ECCV

  1. Cornernet: Detecting objects as paired keypoints, H. Law, J. Deng, [OpenAccess], [Pytorch], CornerNet
  2. Receptive field block net for accurate and fast object detection, S. Liu, D. Huang, Y. Wang, [OpenAccess], [Pytorch], RFBNet
  3. Deep feature pyramid reconfiguration for object detection, T. Kong, F. Sun, W. Huang, H. Liu, [OpenAccess]

2018 Others

  1. YOLOv3: An Incremental Improvement, J. Redmon, A. Farhadi, in: arXiv preprint arXiv:1804.02767, 2018. [OpenAccess], [DarkNet], YOLOv3
  2. Mdssd: Multi-scale deconvolutional single shot detector for small objects, M. Xu, L. Cui, P. Lv, X. Jiang, J. Niu, B. Zhou, M. Wang, in: arXiv preprint arXiv:1805.07009, 2018. [Arxiv], MDSSD

2019 AAAI

  1. M2det: A single-shot object detector based on multi-level feature pyramid network, Q. Zhao, T. Sheng, Y. Wang, Z. Tang, Y. Chen, L. Cai, H. Ling, [OpenAccess], [Pytorch], M2Det
  2. Gradient harmonized single-stage detector, Y. L. Buyu Li, X. Wang, [OpenAccess], [mmdetection ], GHM

2019 CVPR

  1. Feature selective anchor-free module for single-shot object detection, C. Zhu, Y. He, M. Savvides, [OpenAccess], FSFA
  2. Scratchdet: Exploring to train single-shot object detectors from scratch, R. Zhu, S. Zhang, X. Wang, L. Wen, H. Shi, L. Bo, T. Mei, [OpenAccess], [Caffe], Scratchdet
  3. Bottom-up object detection by grouping extreme and center points, X. Zhou, J. Zhuo, P. Krahenbuhl, [OpenAccess], [Pytorch], ExtremeNet
  4. Towards Accurate One-Stage Object Detection with AP-Loss, K. Chen, J. Li, W. Lin, J. See, J. Wang, L. Duan, Z. Chen, C. He, J. Zou, [OpenAccess], AP-Loss

2019 ICCV

  1. Fcos: Fully convolutional one-stage object detection, Z. Tian, C. Shen, H. Chen, T. He, [OpenAccess], [Pytorch], FCOS
  2. RepPoints: Point Set Representation for Object Detection, Z. Yang, S. Liu, H. Hu, L. Wang, S. Lin, [OpenAccess], RepPoints

2019 Others

  1. Objects as points, X. Zhou, D. Wang, P. Krahenb ¨ uhl, in: arXiv preprint arXiv:1904.07850, 2019, [Arxiv], [Pytorch], CenterNet
  2. Centernet: Keypoint triplets for object detection, K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, Q. Tian, in: arXiv preprint arXiv:1904.08189, 2019, [Arxiv], [Pytorch], CenterNet
  3. CornerNet-Lite: Efficient Keypoint Based Object Detection, Hei Law, Yun Teng, Olga Russakovsky, Jia Deng, in: arXiv preprint arXiv:1904.08900, 2019, [OpenAccess], [Pytorch], CornerNet-Lite
  4. Revisiting Feature Alignment for One-stage Object Detection, Y. Chen, C. Han, N. Wang, Z. Zhang, in: arXiv preprint arXiv:1908.01570, 2019, [OpenAccess], AlignDet
  5. PosNeg-Balanced Anchors with Aligned Features for Single-Shot Object Detection, Qiankun Tang, Shice Liu, Jie Li, Yu Hu, in: arXiv preprint arXiv:1908.03295, 2019, [OpenAccess], [Pytorch], PADet
  6. Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection, Q. Tang, S. Liu, J. Li, Y. Hu, in: BMVC, 2019, [OpenAccess], CaRetinaNet

2. Face Detection

  1. Joint face detection and alignment using multi-task cascaded convolutional networks, K. Zhang, Z. Zhang, Z. Li, Y. Qiao， in: IEEE Signal Processing Letters, 2016. [OpenAccess], [Caffe], MTCNN
  2. Detecting faces using region-based fully convolutional networks, Y. Wang, X. Ji, Z. Zhou, H. Wang, Z. Li, in: arXiv preprint arXiv:1709.05256, 2017. [OpenAccess], Face R-FCN
  3. Detecting faces using inside cascaded contextual cnn, K. Zhang, Z. Zhang, H. Wang, Z. Li, Y. Qiao, W. Liu, in: ICCV, 2017. [OpenAccess]
  4. Cms-rcnn: Contextual multiscale region-based cnn for unconstrained face detection, C. Zhu, Y. Zheng, K. Luu, M. Savvides, in: Deep Learning for Biometrics, 2017. [OpenAccess], CMS-RCNN
  5. Face r-cnn, H. Wang, Z. Li, X. Ji, Y. Wang, in: arXiv preprint arXiv:1706.01061, 2017. [OpenAccess], Face R-CNN
  6. Scale-aware face detection, Z. Hao, Y. Liu, H. Qin, J. Yan, X. Li, X. Hu, in: CVPR, 2017. [OpenAccess]
  7. Ssh: Single stage headless face detector, M. Najibi, P. Samangouei, R. Chellappa, L. Davis, in: ICCV, 2017. [OpenAccess], [Caffe], SSH
  8. Feature agglomeration networks for single stage face detection, J. Zhang, X. Wu, J. Zhu, S. C. Hoi, in: arXiv preprint arXiv:1712.00721, 2017. [OpenAccess], FANet
  9. Finding tiny faces, P. Hu, D. Ramanan, [OpenAccess], [MatConvNet], S3FD
  10. S3fd: Single shot scale-invariant face detector, S. Zhang, X. Zhu, Z. Lei, H. Shi, X. Wang, S. Z. Li, [OpenAccess], [Caffe], S3FD
  11. Recurrent scale approximation for object detection in cnn, Y. Liu, H. Li, J. Yan, F. Wei, X. Wang, X. Tang, [OpenAccess], [Caffe], RSA
  12. Anchor cascade for efficient face detection, B. Yu, D. Tao, in: arXiv preprint arXiv:1805.03363, 2018. [OpenAccess]
  13. Face detection using improved faster rcnn, C. Zhang, X. Xu, D. Tu, in: arXiv preprint arXiv:1802.02142, 2018. [OpenAccess], [Caffe]
  14. Face-magnet: Magnifying feature maps to detect small faces, P. Samangouei, M. Najibi, L. Davis, R. Chellappa, in: arXiv preprint arXiv:1803.05258, 2018. [OpenAccess], [Caffe]
  15. Selective refinement network for high performance face detection, C. Chi, S. Zhang, J. Xing, Z. Lei, S. Z. Li, X. Zou, in: arXiv preprint arXiv:1809.02693, 2018. [OpenAccess], [Pytorch], SRN
  16. Pyramidbox: A context-assisted single shot face detector, X. Tang, D. K. Du, Z. He, J. Liu, in: ECCV, 2018. [OpenAccess], [TensorFlow]
  17. Face detection using deep learning: An improved faster rcnn approach, X. Sun, P. Wu, S. C. Hoi, in: Neurocomputing, 2018. [OpenAccess]
  18. Seeing small faces from robust anchors perspective, C. Zhu, R. Tao, K. Luu, M. Savvides, [OpenAccess]
  19. Dsfd: Dual shot face detector, J. Li, Y. Wang, C. Wang, Y. Tai, J. Qian, J. Yang, C. Wang, J. Li, F. Huang, in: CVPR, 2019. [OpenAccess], [Pytorch], DSFD

3. Pedestrian Detection

  1. Bilattice-based logical reasoning for human detection, V. D. Shet, J. Neumann, V. Ramesh, L. S. Davis, in: CVPR, 2007. [OpenAccess]
  2. Integral channel features, P. Dollar, Z. Tu, P. Perona, S. Belongie, in: BMVC, 2009. [OpenAccess], [Project], ICF
  3. A structural filter approach to human detection, G. Duan, H. Ai, S. Lao, in: ECCV, 2010. [OpenAccess]
  4. Multi-cue pedestrian classification with partial occlusion handling, M. Enzweiler, A. Eigenstetter, B. Schiele, D. M. Gavrila, in: CVPR, 2010. [OpenAccess]
  5. A discriminative deep model for pedestrian detection with occlusion handling, W. Ouyang, X. Wang, in: CVPR, 2012. [OpenAccess]
  6. Modeling mutual visibility relationship in pedestrian detection, W. Ouyang, X. Zeng, X. Wang, in: CVPR, 2013. [OpenAccess]
  7. Single-pedestrian detection aided by multi-pedestrian detection, W. Ouyang, X. Wang, in: CVPR, 2013. [OpenAccess]
  8. Pedestrian detection with unsupervised multi-stage feature learning, P. Sermanet, K. Kavukcuoglu, S. Chintala, Y. LeCun, in: CVPR, 2013. [OpenAccess]
  9. Joint deep learning for pedestrian detection, W. Ouyang, X. Wang, in: ICCV, 2013. [OpenAccess]
  10. Handling occlusions with franken-classifiers, M. Mathias, R. Benenson, R. Timofte, L. Van Gool, in: ICCV, 2013. [OpenAccess]
  11. Ten years of pedestrian detection, what have we learned?, R. Benenson, M. Omran, J. Hosang, B. Schiele, in: ECCV, 2014. [OpenAccess]
  12. Detection and tracking of occluded people, S. Tang, M. Andriluka, B. Schiele, in: IJCV, 2014. [OpenAccess]
  13. Learning complexity-aware cascades for deep pedestrian detection, Z. Cai, M. Saberian, N. Vasconcelos, in: ICCV, 2015. [OpenAccess]
  14. Taking a deeper look at pedestrians, J. Hosang, M. Omran, R. Benenson, B. Schiele, [OpenAccess]
  15. Deep learning strong parts for pedestrian detection, Y. Tian, P. Luo, X. Wang, X. Tang, in: CVPR, 2015. [OpenAccess]
  16. A unified multi-scale deep convolutional neural network for fast object detection, Z. Cai, Q. Fan, R. S. Feris, N. Vasconcelos, [OpenAccess], [Caffe], MSCNN
  17. Dave: A unified framework for fast vehicle detection and annotation, Y. Zhou, L. Liu, L. Shao, M. Mellor, in: ECCV, 2016. [OpenAccess]
  18. Is faster r-cnn doing well for pedestrian detection?, L. Zhang, L. Lin, X. Liang, K. He, in: ECCV, 2016. [OpenAccess], [Caffe]
  19. Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers, F. Yang, W. Choi, Y. Lin, [ OpenAccess], SDP-CRC
  20. Accurate single stage detector using recurrent rolling convolution, J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y.-W. Tai, L. Xu, [OpenAccess], [Caffe], RRC
  21. What can help pedestrian detection?, J. Mao, T. Xiao, Y. Jiang, Z. Cao, in: CVPR, 2017. [OpenAccess]
  22. Learning cross-modal deep representations for robust pedestrian detection, D. Xu, W. Ouyang, E. Ricci, X. Wang, N. Sebe, in: CVPR, 2017. [OpenAccess], [Caffe], CMT-CNN
  23. Repulsion loss: Detecting pedestrians in a crowd, X. Wang, T. Xiao, Y. Jiang, S. Shao, J. Sun, C. Shen, in: CVPR, 2018. [OpenAccess], [Pytorch]
  24. Bi-box regression for pedestrian detection and occlusion estimation, C. Zhou, J. Yuan, in: ECCV, 2018. [OpenAccess]
  25. Occlusion-aware r-cnn: Detecting pedestrians in a crowd, S. Zhang, L. Wen, X. Bian, Z. Lei, S. Z. Li, in: ECCV, 2018. [OpenAccess], OR R-CNN
  26. Scale-aware fast r-cnn for pedestrian detection, J. Li, X. Liang, S. Shen, T. Xu, J. Feng, S. Yan, [Arxiv], in: TMM, 2018. SAF R-CNN
  27. Pcn: Part and context information for pedestrian detection with cnns, S. Wang, J. Cheng, H. Liu, M. Tang, in: arXiv preprint arXiv:1804.04483, 2018. [OpenAccess]

4 Benchmarks

4.1 Generic Detection Datasets

  1. Pascal VOC: The pascal visual object classes (voc) challenge, M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman, [OpenAccess], [Project]
  2. ImageNet: Imagenet: A large-scale hierarchical image database, * J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei*, [OpenAccess], [Project]
  3. MSCOCO: Microsoft COCO: Common Objects in Context, T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, C. L. Zitnick, [OpenAccess], [Project]
  4. Open Images: The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale, A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, T. Duerig, et al., [OpenAccess], [Project]
  5. LVIS: Lvis: A dataset for large vocabulary instance segmentation, A. Gupta, P. Dollar, R. Girshick, [OpenAccess], [Project]

4.2 Face Detection Datasets

  1. WIDER FACE: Wider face: A face detection benchmark, S. Yang, P. Luo, C.-C. Loy, X. Tang, [OpenAccess], [Project]
  2. FDDB: Fddb: A benchmark for face detection in unconstrained settings, V. Jain, E. Learned-Miller, [OpenAccess], [Project]
  3. PASCAL FACE: The pascal visual object classes (voc) challenge, M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman, [OpenAccess], [Project]
  4. MALF: Automatic Face and Gesture Recognition (FG), Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan Z, [OpenAccess], [Project]
  5. AFW: Face detection, pose estimation and landmark localization in the wild, X. Zhu, D. Ramanan, [OpenAccess], [Project]

4.3 Pedestrian Detection Datasets

  1. CityPersons: Citypersons: A diverse dataset for pedestrian detection, S. Zhang, R. Benenson, B. Schiele, [OpenAccess], [Project]
  2. Caltech: Pedestrian detection: An evaluation of the state of the art, P. Dollar, C. Wojek, B. Schiele, P. Perona, [OpenAccess], [Project]
  3. ETH: Depth and appearance for mobile scene analysis, A. Ess, B. Leibe, L. Van Gool, [OpenAccess], [Project]
  4. INRIA: Histograms of oriented gradients for human detection, N. Dalal, B. Triggs, [OpenAccess], [Project]
  5. KITTI: Vision meets robotics: The kitti dataset, A. Geiger, P. Lenz, C. Stiller, R. Urtasun, [OpenAccess], [Project]

5. SOTA

5.1 Pascal VOC

Method Backbone Proposed Year Input size(Test) VOC2007 VOC2012
Two-stage
R-CNN VGG-16 2014 Arbitrary 66.0∗ 62.4†
SPP-net VGG-16 2014 ~600 × 1000 63.1∗ -
Fast R-CNN VGG-16 2015 ~600 × 1000 70.0 68.4
Faster R-CNN VGG-16 2015 ~600 × 1000 73.2 70.4
MR-CNN VGG-16 2015 Multi-Scale 78.2 73.9
Faster R-CNN ResNet-101 2016 ~600 × 1000 76.4 73.8
R-FCN ResNet-101 2016 ~600 × 1000 80.5 77.6
OHEM VGG-16 2016 ~600 × 1000 74.6 71.9
HyperNet VGG-16 2016 ~600 × 1000 76.3 71.4
ION VGG-16 2016 ~600 × 1000 79.2 76.4
CRAFT VGG-16 2016 ~600 × 1000 75.7 71.3†
LocNet VGG-16 2016 ~600 × 1000 78.4 74.8†
R-FCN w DCN ResNet-101 2017 ~600 × 1000 82.6 -
CoupleNet ResNet-101 2017 ~600 × 1000 82.7 80.4
DeNet512(wide) ResNet-101 2017 ~512 × 512 77.1 73.9
FPN-Reconfig ResNet-101 2018 ~600 × 1000 82.4 81.1
DeepRegionLet ResNet-101 2018 ~600 × 1000 83.3 81.3
DCN+R-CNN ResNet-101+ResNet-152 2018 Arbitrary 84.0 81.2
One-stage
YOLOv1 VGG16 2016 448 × 448 66.4 57.9
SSD512 VGG-16 2016 512 × 512 79.8 78.5
YOLOv2 Darknet 2017 544 × 544 78.6 73.5
DSSD513 ResNet-101 2017 513 × 513 81.5 80.0
DSOD300 DS/64-192-48-1 2017 300 × 300 77.7 76.3
RON384 VGG-16 2017 384 × 384 75.4 73.0
STDN513 DenseNet-169 2018 513 × 513 80.9 -
RefineDet512 VGG-16 2018 512 × 512 81.8 80.1
RFBNet512 VGG16 2018 512 × 512 82.2 -
CenterNet ResNet101 2019 512 × 512 78.7 -
CenterNet DLA 2019 512 × 512 80.7 -

∗: This entry reports the the model is trained with VOC2007 trainval sets only. †: This entry reports the the model are trained with VOC2012 trainval sets only .

5.2 MSCOCO

Method Backbone Year AP AP$_{50}$ AP$_{75}$ AP$_{S}$ AP$_{M}$ AP$_{L}$
Two-stage
Fast R-CNN VGG-16 2015 19.7 35.9 - - - -
Faster R-CNN VGG-16 2015 21.9 42.7 - - - -
OHEM VGG-16 2016 22.6 42.5 22.2 5.0 23.7 37.9
ION VGG-16 2016 23.6 43.2 23.6 6.4 24.1 38.3
OHEM++ VGG-16 2016 25.5 45.9 26.1 7.4 27.7 40.3
R-FCN ResNet-101 2016 29.9 51.9 - 10.8 32.8 45.0
Faster R-CNN+++ ResNet-101 2016 34.9 55.7 37.4 15.6 38.7 50.9
Faster R-CNN w FPN ResNet-101 2016 36.2 59.1 39.0 18.2 39.0 48.2
DeNet-101(wide) ResNet-101 2017 33.8 53.4 36.1 12.3 36.1 50.8
CoupleNet ResNet-101 2017 34.4 54.8 37.2 13.4 38.1 50.8
Faster R-CNN by G-RMI Inception-ResNet-v2 2017 34.7 55.5 36.7 13.5 38.1 52.0
Deformable R-FCN Aligned-Inception-ResNet 2017 37.5 58.0 40.8 19.4 40.1 52.5
Mask-RCNN ResNeXt-101 2017 39.8 62.3 43.4 22.1 43.2 51.2
umd det ResNet-101 2017 40.8 62.4 44.9 23.0 43.4 53.2
Fitness-NMS ResNet-101 2017 41.8 60.9 44.9 21.5 45.0 57.5
DCN w Relation Net ResNet-101 2018 39.0 58.6 42.9 - - -
DeepRegionlets ResNet-101 2018 39.3 59.8 - 21.7 43.7 50.9
C-Mask RCNN ResNet-101 2018 42.0 62.9 46.4 23.4 44.7 53.8
Group Norm ResNet-101 2018 42.3 62.8 46.2 - - -
DCN+R-CNN ResNet-101+ResNet-152 2018 42.6 65.3 46.5 26.4 46.1 56.4
Cascade R-CNN ResNet-101 2018 42.8 62.1 46.3 23.7 45.5 55.2
SNIP++ DPN-98 2018 45.7 67.3 51.1 29.3 48.8 57.1
SNIPER++ ResNet-101 2018 46.1 67.0 51.6 29.6 48.9 58.1
PANet++ ResNeXt-101 2018 47.4 67.2 51.8 30.1 51.7 60.0
Grid R-CNN ResNeXt-101 2019 43.2 63.0 46.6 25.1 46.5 55.2
DCN-v2 ResNet-101 2019 44.8 66.3 48.8 24.4 48.1 59.6
DCN-v2++ ResNet-101 2019 46.0 67.9 50.8 27.8 49.1 59.5
TridentNet ResNet-101 2019 42.7 63.6 46.5 23.9 46.6 56.6
TridentNet ResNet-101-Deformable 2019 48.4 69.7 53.5 31.8 51.3 60.3
One-stage
SSD512 VGG-16 2016 28.8 48.5 30.3 10.9 31.8 43.5
RON384++ VGG-16 2017 27.4 49.5 27.1 - - -
YOLOv2 DarkNet-19 2017 21.6 44.0 19.2 5.0 22.4 35.5
SSD513 ResNet-101 2017 31.2 50.4 33.3 10.2 34.5 49.8
DSSD513 ResNet-101 2017 33.2 53.3 35.2 13.0 35.4 51.1
RetinaNet800++ ResNet-101 2017 39.1 59.1 42.3 21.8 42.7 50.2
STDN513 DenseNet-169 2018 31.8 51.0 33.6 14.4 36.1 43.4
FPN-Reconfig ResNet-101 2018 34.6 54.3 37.3 - - -
RefineDet512 ResNet-101 2018 36.4 57.5 39.5 16.6 39.9 51.4
RefineDet512++ ResNet-101 2018 41.8 62.9 45.7 25.6 45.1 54.1
GHM SSD ResNeXt-101 2018 41.6 62.8 44.2 22.3 45.1 55.3
CornerNet511 Hourglass-104 2018 40.5 56.5 43.1 19.4 42.7 53.9
CornerNet511++ Hourglass-104 2018 42.1 57.8 45.3 20.8 44.8 56.7
M2Det800 VGG-16 2019 41.0 59.7 45.0 22.1 46.5 53.8
M2Det800++ VGG-16 2019 44.2 64.6 49.3 29.2 47.9 55.1
ExtremeNet Hourglass-104 2019 40.2 55.5 43.2 20.4 43.2 53.1
CenterNet-HG Hourglass-104 2019 42.1 61.1 45.9 24.1 45.5 52.8
FCOS ResNeXt-101 2019 42.1 62.1 45.2 25.6 44.9 52.0
FSAF ResNeXt-101 2019 42.9 63.8 46.3 26.6 46.2 52.7
CenterNet511 Hourglass-104 2019 44.9 62.4 48.1 25.6 47.4 57.4
CenterNet511++ Hourglass-104 2019 47.0 64.5 50.7 28.9 49.9 58.9

6. Emerging Ideas

6.1 Anchor Design

6.1.1 Anchor-Free Methods

  1. Denet: Scalable real-time object detection with directed sparse sampling, L. Tychsen-Smith, L. Petersson, in: ICCV, 2017. [OpenAccess],[Theano], DeNet
  2. Cornernet: Detecting objects as paired keypoints, H. Law, J. Deng, in: ECCV, 2018. [OpenAccess], [Pytorch], CornerNet
  3. Objects as points, X. Zhou, D. Wang, P. Krahenb ¨ uhl, [Arxiv], [Pytorch], in: arXiv preprint arXiv:1904.07850, 2019. CenterNet
  4. Centernet: Keypoint triplets for object detection, K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, Q. Tian, in: arXiv preprint arXiv:1904.08189, 2019. [Arxiv], [Pytorch], CenterNet
  5. Bottom-up object detection by grouping extreme and center points, X. Zhou, J. Zhuo, P. Krahenbuhl, in: CVPR, 2019. [OpenAccess], [Pytorch], ExtremeNet
  6. Feature selective anchor-free module for single-shot object detection, C. Zhu, Y. He, M. Savvides, in: CVPR, 2019. [OpenAccess], FSFA
  7. Fcos: Fully convolutional one-stage object detection, Z. Tian, C. Shen, H. Chen, T. He, in: ICCV, 2019. [OpenAccess], [Pytorch], FCOS
  8. CornerNet-Lite: Efficient Keypoint Based Object Detection, Hei Law, Yun Teng, Olga Russakovsky, Jia Deng, in: arXiv preprint arXiv:1904.08900, 2019. [OpenAccess], [Pytorch], CornerNet-Lite
  9. RepPoints: Point Set Representation for Object Detection, Z. Yang, S. Liu, H. Hu, L. Wang, S. Lin, in: ICCV, 2019. [OpenAccess], RepPoints

6.1.2 Anchor-Refinement Methods

  1. Yolo9000: better, faster, stronger, J. Redmon, A. Farhadi, [OpenAccess], [DarkNet], in: CVPR, 2017. YOLOv2
  2. Cascade r-cnn: Delving into high quality object detection, Z. Cai, N. Vasconcelos, in: CVPR, 2018. [OpenAccess], [Caffe], [Caffe2] Cascade R-CNN
  3. Single-shot refinement neural network for object detection, S. Zhang, L. Wen, X. Bian, Z. Lei, S. Z. Li, in: CVPR, 2018. [OpenAccess], [Caffe], RefineDet
  4. Metaanchor: Learning to detect objects with customized anchors, T. Yang, X. Zhang, Z. Li, W. Zhang, J. Sun, in: NeurIPS, 2018. [OpenAccess], MetaAnchor
  5. Derpn: Taking a further step toward more general object detection, L. J. Z. X. Lele Xie, Yuliang Liu, in: AAAI, 2019. [OpenAccess], [Caffe], DeRPN
  6. Region Proposal by Guided Anchoring, J. Wang, K. Chen, S. Yang, C. C. Loy, D. Lin, [OpenAccess], [mmdetection]
  7. Revisiting Feature Alignment for One-stage Object Detection, Y. Chen, C. Han, N. Wang, Z. Zhang, in: arXiv preprint arXiv:1908.01570, 2019, [OpenAccess], AlignDet
  8. PosNeg-Balanced Anchors with Aligned Features for Single-Shot Object Detection, Qiankun Tang, Shice Liu, Jie Li, Yu Hu, in: arXiv preprint arXiv:1908.03295, 2019, [OpenAccess], [Pytorch], PADet
  9. Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection, Q. Tang, S. Liu, J. Li, Y. Hu, in: BMVC, 2019, [OpenAccess], CaRetinaNet

6.2 AutoML Detection

  1. Nas-fpn: Learning scalable feature pyramid architecture for object detection, G. Ghiasi, T.-Y. Lin, Q. V. Le, [OpenAccess], [TensorFlow], NAS-FPN
  2. Detnas: Neural architecture search on object detection, Y. Chen, T. Yang, X. Zhang, G. Meng, C. Pan, J. Sun, in: arXiv preprint arXiv:1903.10979, 2019. [OpenAccess], DetNas
  3. Learning data augmentation strategy, B. Zoph, E. D. Cubuk, G. Ghiasi, T.-Y. Lin, J. Shlens, Q. V. Le, in: arXiv preprint arXiv:1906.11172, 2019. [OpenAccess], [TensorFlow]
  4. AutoAugment: Learning Augmentation Strategies from Data, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, Q. V. Le, in: CVPR, 2019. [OpenAccess], AutoAugment

6.3 Low-shot Detection

  1. Few-example object detection with model communication, X. Dong, L. Zheng, F. Ma, Y. Yang, D. Meng, in: TPAMI, 2018. [OpenAccess], [Project], MSPLD
  2. Lstd: A low-shot transfer detector for object detection, H. Chen, Y. Wang, G. Wang, Y. Qiao, in: AAAI, 2018. [OpenAccess], [Caffe], LSTD
  3. Repmet: Representative-based metric learning for classification and one-shot object detection, E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, S. Pankanti, R. Feris, A. Kumar, R. Giries, A. M. Bronstein, in: CVPR, 2019. [OpenAccess], [Pytorch], RepMet

6.4 Others

  1. Megdet: A large mini-batch object detector, C. Peng, T. Xiao, Z. Li, Y. Jiang, X. Zhang, K. Jia, G. Yu, J. Sun, in: CVPR, 2018 [OpenAccess], Megdet
  2. Incremental learning of object detectors without catastrophic forgetting, *K. Shmelkov, C. Schmid, K. Alahari, *, in: ICCV, 2017. [OpenAccess], [TensorFlow]

7. Other Resources

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