Object detection
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.
![](../I/m/Detected-with-YOLO--Schreibtisch-mit-Objekten.jpg)
Uses
It is widely used in computer vision tasks such as image annotation,[2] activity recognition,[3] 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.
Concept
Every object class has its own special features that helps 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
![](../I/m/Comparison_of_speed_and_accuracy_of_detectors.png)
Methods for object detection generally fall into either machine learning-based approaches or deep learning-based approaches. For Machine Learning 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, deep learning techniques are able to do end-to-end object detection without specifically defining features, and are typically based on convolutional neural networks (CNN).
- Machine learning approaches:
- Deep learning approaches:
See also
References
- Dasiopoulou, Stamatia, et al. "Knowledge-assisted semantic video object detection." IEEE Transactions on Circuits and Systems for Video Technology 15.10 (2005): 1210–1224.
- 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.
- Wu, Jianxin, et al. "A scalable approach to activity recognition based on object use." 2007 IEEE 11th international conference on computer vision. IEEE, 2007.
- Bochkovskiy, Alexey (2020). "Yolov4: Optimal Speed and Accuracy of Object Detection". arXiv:2004.10934 [cs.CV].
- Dalal, Navneet (2005). "Histograms of oriented gradients for human detection" (PDF). Computer Vision and Pattern Recognition. 1.
- 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: 580–587. arXiv:1311.2524. doi:10.1109/CVPR.2014.81. ISBN 978-1-4799-5118-5.
- Girschick, Ross (2015). "Fast R-CNN" (PDF). Proceedings of the IEEE International Conference on Computer Vision: 1440–1448. arXiv:1504.08083. Bibcode:2015arXiv150408083G.
- Shaoqing, Ren (2015). "Faster R-CNN". Advances in Neural Information Processing Systems. arXiv:1506.01497.
- 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.org. Retrieved 2020-05-23.
- Liu, Wei (October 2016). SSD: Single shot multibox detector. European Conference on Computer Vision. Lecture Notes in Computer Science. 9905. pp. 21–37. arXiv:1512.02325. doi:10.1007/978-3-319-46448-0_2. ISBN 978-3-319-46447-3.
- Redmon, Joseph (2016). "You only look once: Unified, real-time object detection". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. arXiv:1506.02640. Bibcode:2015arXiv150602640R.
- Redmon, Joseph (2017). "YOLO9000: better, faster, stronger". arXiv:1612.08242 [cs.CV].
- Redmon, Joseph (2018). "Yolov3: An incremental improvement". arXiv:1804.02767 [cs.CV].
- Zhang, Shifeng (2018). "Single-Shot Refinement Neural Network for Object Detection". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 4203–4212. arXiv:1711.06897. Bibcode:2017arXiv171106897Z.
- 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.
- Zhu, Xizhou (2018). "Deformable ConvNets v2: More Deformable, Better Results". arXiv:1811.11168 [cs.CV].
- Dai, Jifeng (2017). "Deformable Convolutional Networks". arXiv:1703.06211 [cs.CV].
- "Object Class Detection". Vision.eecs.ucf.edu. Archived from the original on 2013-07-14. Retrieved 2013-10-09.
- "ETHZ – Computer Vision Lab: Publications". Vision.ee.ethz.ch. Archived from the original on 2013-06-03. Retrieved 2013-10-09.