A Review on Various Techniques for Object Detection

  • Soumya kelur
  • Ranjan Kumar H S
Keywords: Support vector machine (SVM); Human visual system; object detection techniques; image processing; computer vision;

Abstract

Detection of object and recognition of objects in real world computing environment is one of the important tasks in computer vision. To solve this task there are many challenges in designing algorithm, we have to introduce different and innovative algorithms to detect objects in natural environments. Detection of object is a computer technology which is related to the computer vision and image processing. This paper provides a brief description of techniques, methods and algorithms used in object detection. At last it highlights the importance of selective image encryption

References

[1] Jianan Li, Xiaodan L, Jianshu Li, Yunchao, Jiashi Feng, and Shuicheng Yan.. multi-stage object detection with group recursive learning. fellow member, IEEE, pp. 1520-9210
[2] Anusha Alexander, Meher Madhu Dharmana. object detection algorithm for segregating similar coloured objects and database formation.” 2017 international conference on circuits power and computing technologies [ICCPCT]
[3] Chi-chi Sun, Yi-Hua Wang, and Ming-Hwa Sheu. Fast motion object detection algorithm. In IEEE sensor journal vol. 17. No. 17. September 2017
[4] Ye Xiufen, Wang Sheng. Small object detection algorithm for sonar image based on pixel hierarchy. Proceedings of the 34th chinese control conference july 28-30, 2015.
[5] Wei Zhu, Quian-Liang Fu. The 28th research institute of china electronics technology group corporation in IEEE 2016.
[6] Ross Girshick, Jeff Donahue, Trevor Darrell Jitendra Malik. Rich features for accurate object detection and semantic segmentation. In CVPR 2014.
[7] Anuj Mohan, Constantine Papageorgiou, and Tomaso Poggio. Example based object detection in images. In IEEE transactions on pattern analysis and machine intelligence, vol. 23, no. 4, April 2001.
[8] Ian Fasel, Bret Fortenberry, Javier Movellan. A generative frame work for real time object detection and classification. In conference on computer vision and image understanding 98 (2005) 182-210.
[9] Pingping Zhang, Dong Wang, Huchuan Lu, Hongyu Wang. Aggreagating multi level convolutional features for salient object detection. In 2017 IEEE international conference on computer vision.
[10] J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.
[11] Antonio Torralba, Kein P. Murthy, William t. Freeman. Efficient boosting procedures for multiclass object detection. In computer science and artificial intelligence lab, MIT, Cambridge, MA 2139.
[12] Yann Le Cun, Fu Jie Hung, Leon Bottu. Learning methods for generic object recognition with invariance to pose and lighting. In IEEE computer vision and pattern recognition in 2004 (CVPR’04).
[13] Kobi Levi, Yair Weiss. Learning object detection from small number of examples. Proceedings of the 2004 IEEE conference on computer vision and pattern recognition.
[14] Li Hou, Wanggen Wan, Kang Han, Rizwan Muhammad, Mingyang Yang. Human detection and tracking over camera networks. In IEEE 2016 conference on computer vision and pattern recognition (ICALP 2016).
[15] Viola P, Jones M. rapid object detection using boosted cascade of simple feature. IEEE conference on computer vision and pattern recognition.
[16] Wang Zhiqiang, Liu Jun. a review of object detection based on convolutional neural network. In proceedings of 36th Chinese control conference July 26-28, 2017, dalian, china.
Published
2018-11-02
How to Cite
kelur, S., & S, R. K. H. (2018, November 2). A Review on Various Techniques for Object Detection. ASIAN JOURNAL FOR CONVERGENCE IN TECHNOLOGY (AJCT ) -UGC LISTED, 4(II). Retrieved from http://asianssr.org/index.php/ajct/article/view/551