A Brief History of Object Detection – from Haar-like features to losing anchors

Hello, my name is Tommi Kerola, an engineer at Preferred Networks. I would like to share some slides about recent research in object detection that was presented at an internal PFN seminar. We are making the slides publicly available with the hope that others may find it interesting or useful for research purposes.

Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.

A Brief History of Object Detection / Tommi Kerola from Preferred Networks

References:

• T. Akiba et al. Pfdet:  2nd place solution to open images challenge 2018 object detection track. arXiv preprint arXiv:1809.00778, 2018.
• N. Bodla et al. Soft-nms–improving object detection with one line of code. In Proceedings of the IEEE International Conference on Computer Vision, pages 5561–5569, 2017.
• N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In International Conference on computer vision & Pattern Recognition (CVPR’05) , volume 1, pages 886–893. IEEE Computer Society, 2005.
• K. Duan et al. Centernet:  Object detection with keypoint triplets. arXiv preprint arXiv:1904.08189, 2019.
• P. Felzenszwalb et al. A discriminatively trained, multiscale, deformable part model. In CVPR, 2008.
• R. Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448, 2015

• R. Girshick et al.Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580–587, 2014.
• J. Hosang et al. Learning non-maximum suppression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4507–4515, 2017.
• H. Hu et al. Relation networks for object detection.In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3588–3597, 2018.
• A. Kirillov et al.Panoptic segmentation.In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019.
• A. Krizhevsky et al. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
• H. Law and J. Deng. Cornernet:  Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV), pages 734–750, 2018

• Y. Li et al. Scale-aware trident networks for object detection.arXiv preprint arXiv:1901.01892, 2019.
• T.-Y. Lin et al. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2117–2125, 2017a.
• T.-Y. Lin et al. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980–2988, 2017b.
• W. Liu et al. Ssd:  Single shot multibox detector. In European conference on computer vision, pages 21–37. Springer, 2016.
• X. Lu et al.Grid r-cnn.In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
• A. Newell and J. Deng. Pixels to graphs by associative embedding. In Advances in neural information processing systems, pages 2171–2180, 2017.

• A. Newell et al.Associative embedding:  End-to-end learning for joint detection and grouping. In Advances in Neural Information Processing Systems, pages 2277–2287, 2017.
• J. Redmon and A. Farhadi. Yolo9000:  better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 7263–7271, 2017.
• J. Redmon et al. You only look once:  Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788, 2016.
• S. Ren et al. Faster r-cnn:  Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91–99, 2015. B. Singh et al.

• Sniper:  Efficient multi-scale training.In Advances in Neural Information Processing Systems , pages 9310–9320, 2018.

• Z. Tian et al.Fcos:  Fully convolutional one-stage object detection.arXiv preprint arXiv:1904.01355, 2019
• J. Uijlings et al. Selective search for object recognition. International journal of computer vision , 104(2):154–171, 2013.
• P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. CVPR, 2001.
• S. Zhang et al. Single-shot refinement neural network for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 4203–4212, 2018.
• H. Zhou et al. Cad:  Scale invariant framework for real-time object detection. In The IEEE International Conference on Computer Vision (ICCV) Workshops , Oct 2017.
• X. Zhou et al. Bottom-up object detection by grouping extreme and center points. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019a.
• X. Zhou et al. Objects as points. arXiv preprint arXiv:1904.07850 , 2019b
• Z. Zou et al. Object detection in 20 years:  A survey. arXiv preprint arXiv:1905.05055, 2019