Object detection

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Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.[1] Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

Uses[edit]

Detection of objects on a road

It is widely used in computer vision tasks such as image annotation,[2] vehicle counting,[3] activity recognition,[4] face detection, face recognition, video object co-segmentation. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, or tracking a person in a video.

Often, the test images are sampled from a different data distribution, making the object detection task significantly more difficult.[5] To address the challenges caused by the domain gap between training and test data, many unsupervised domain adaptation approaches have been proposed.[5][6][7][8][9] A simple and straightforward solution of reducing the domain gap is to apply an image-to-image translation approach, such as cycle-GAN.[10] Among other uses, cross-domain object detection is applied in autonomous driving, where models can be trained on a vast amount of video game scenes, since the labels can be generated without manual labor.

Concept[edit]

Every object class has its own special features that help in classifying the class – for example all circles are round. Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are perpendicular at corners and have equal side lengths are needed. A similar approach is used for face identification where eyes, nose, and lips can be found and features like skin color and distance between eyes can be found.

Methods[edit]

Methods for object detection generally fall into either neural network-based or non-neural approaches. For non-neural approaches, it becomes necessary to first define features using one of the methods below, then using a technique such as support vector machine (SVM) to do the classification. On the other hand, neural techniques are able to do end-to-end object detection without specifically defining features, and are typically based on convolutional neural networks (CNN).

See also[edit]

References[edit]

  1. ^ Dasiopoulou, Stamatia, et al. "Knowledge-assisted semantic video object detection." IEEE Transactions on Circuits and Systems for Video Technology 15.10 (2005): 1210–1224.
  2. ^ Ling Guan; Yifeng He; Sun-Yuan Kung (1 March 2012). Multimedia Image and Video Processing. CRC Press. pp. 331–. ISBN 978-1-4398-3087-1.
  3. ^ Alsanabani, Ala; Ahmed, Mohammed; AL Smadi, Ahmad (2020). "Vehicle Counting Using Detecting-Tracking Combinations: A Comparative Analysis". 2020 the 4th International Conference on Video and Image Processing. pp. 48–54. doi:10.1145/3447450.3447458. ISBN 9781450389075. S2CID 233194604.
  4. ^ Wu, Jianxin, et al. "A scalable approach to activity recognition based on object use." 2007 IEEE 11th international conference on computer vision. IEEE, 2007.
  5. ^ a b Oza, Poojan; Sindagi, Vishwanath A.; VS, Vibashan; Patel, Vishal M. (2021-07-04). "Unsupervised Domain Adaptation of Object Detectors: A Survey". arXiv:2105.13502 [cs.CV].
  6. ^ Khodabandeh, Mehran; Vahdat, Arash; Ranjbar, Mani; Macready, William G. (2019-11-18). "A Robust Learning Approach to Domain Adaptive Object Detection". arXiv:1904.02361 [cs.LG].
  7. ^ Soviany, Petru; Ionescu, Radu Tudor; Rota, Paolo; Sebe, Nicu (2021-03-01). "Curriculum self-paced learning for cross-domain object detection". Computer Vision and Image Understanding. 204: 103166. arXiv:1911.06849. doi:10.1016/j.cviu.2021.103166. ISSN 1077-3142. S2CID 208138033.
  8. ^ Menke, Maximilian; Wenzel, Thomas; Schwung, Andreas (October 2022). "Improving GAN-based Domain Adaptation for Object Detection". 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). pp. 3880–3885. doi:10.1109/ITSC55140.2022.9922138. ISBN 978-1-6654-6880-0. S2CID 253251380.
  9. ^ Menke, Maximilian; Wenzel, Thomas; Schwung, Andreas (2022-08-31). "AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection". arXiv:2208.14662 [cs.CV].
  10. ^ Zhu, Jun-Yan; Park, Taesung; Isola, Phillip; Efros, Alexei A. (2020-08-24). "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks". arXiv:1703.10593 [cs.CV].
  11. ^ Dalal, Navneet (2005). "Histograms of oriented gradients for human detection" (PDF). Computer Vision and Pattern Recognition. 1.
  12. ^ Ross, Girshick (2014). "Rich feature hierarchies for accurate object detection and semantic segmentation" (PDF). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. pp. 580–587. arXiv:1311.2524. doi:10.1109/CVPR.2014.81. ISBN 978-1-4799-5118-5. S2CID 215827080.
  13. ^ Girschick, Ross (2015). "Fast R-CNN" (PDF). Proceedings of the IEEE International Conference on Computer Vision. pp. 1440–1448. arXiv:1504.08083. Bibcode:2015arXiv150408083G.
  14. ^ Shaoqing, Ren (2015). "Faster R-CNN". Advances in Neural Information Processing Systems. arXiv:1506.01497.
  15. ^ a b Pang, Jiangmiao; Chen, Kai; Shi, Jianping; Feng, Huajun; Ouyang, Wanli; Lin, Dahua (2019-04-04). "Libra R-CNN: Towards Balanced Learning for Object Detection". arXiv:1904.02701v1 [cs.CV].
  16. ^ Liu, Wei (October 2016). "SSD: Single Shot MultiBox Detector". Computer Vision – ECCV 2016. Lecture Notes in Computer Science. Vol. 9905. pp. 21–37. arXiv:1512.02325. doi:10.1007/978-3-319-46448-0_2. ISBN 978-3-319-46447-3. S2CID 2141740.
  17. ^ Zhang, Shifeng (2018). "Single-Shot Refinement Neural Network for Object Detection". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4203–4212. arXiv:1711.06897. Bibcode:2017arXiv171106897Z.
  18. ^ Lin, Tsung-Yi (2020). "Focal Loss for Dense Object Detection". IEEE Transactions on Pattern Analysis and Machine Intelligence. 42 (2): 318–327. arXiv:1708.02002. Bibcode:2017arXiv170802002L. doi:10.1109/TPAMI.2018.2858826. PMID 30040631. S2CID 47252984.
  19. ^ Zhu, Xizhou (2018). "Deformable ConvNets v2: More Deformable, Better Results". arXiv:1811.11168 [cs.CV].
  20. ^ Dai, Jifeng (2017). "Deformable Convolutional Networks". arXiv:1703.06211 [cs.CV].

External links[edit]

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