Detection of Human Behaviour by Object Recognition using Deep Learning: A Review

Mr. Utsab Mukherjee

doi:10.21276/ijircst.2020.8.2.2

Abstract

The major drawback of the society is the falsehood of human nature; so prediction of nature of the individual by analysing the video or image of that person is highly necessary. From the post World War II period due to the advancement of technology since the past few decades many countries have developed low cost cameras with high resolution. They generally use the RGB and depth features to enhance the image quality captured by the camera. Hence object recognition is the new branch of computer science which emerges. It has got a close relationship with image understanding and analysis of video; which encourages several researchers to work in this domain since past few years. As the branch of deep learning develops handful of efficient tools have been develop which prove to be highly efficient to learn high level deeper features of the image and semantics. In this paper a review on object recognition using deep learning has been discussed. This paper includes basics of deep learning and object recognition. Then we have discussed some tools required to perform object recognition as well as moving object recognition and finally some future goals of this subject have been discussed.

Keywords

Deep Learning, Object Detection, Neural Network and Object Recognition

References

1.	P. F. Felzenszwalb, R. B. Girshick, D. Mcallester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, p. 1627, 2010.
2.	Object Detection with Deep Learning: A Review Zhong-Qiu Zhao, Member, IEEE, Peng Zheng, Shou-tao Xu, and Xindong Wu, Fellow, IEEE
3.	LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. nature, 521(7553), pp.436-444
4.	D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Comput. Vision,vol. 60, no. 2, pp. 91–110, 2004.
5.	N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in CVPR, 2005.
6.	R. Lienhart and J. Maydt, “An extended set of haar – like features for rapid object detection,” in ICIP, 2002.
7.	C. Cortes and V. Vapnik, “Support vector machine, ” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
8.	Y. Freund and R. E. Schapire, “A desicion-theoretic generalization of on-line learning and an application to boosting,” J. of Comput. & Sys. Sci., vol. 13, no. 5, pp. 663–671, 1997.
9.	M. Pandey and S. Lazebnik, "Scene recognition and weakly supervised object localization with deformable part-based models," 2011 International Conference on Computer Vision, Barcelona, 2011, pp. 1307-1314
10.	A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classiï¬cation with deep convolutional neural networks,” in NIPS, 2012.
11.	R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in CVPR, 2014.
12.	J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Uniï¬ed, real-time object detection,” in CVPR, 2016.
13.	Mandal, M., Kumar, L.K. and Saran, M.S., 2020. MotionRec: A Unified Deep Framework for Moving Object Recognition. In The IEEE Winter Conference on Applications of Computer Vision (pp. 2734-2743).
14.	R.Muralidharan, Dr.C.Chandrasekar, Object Recognition using SVM-KNN based on Geometric Moment Invariant,International Journal of Computer Trends and Technology-July to Aug Issue 2011 ISSN:2231-2803 http://www.internationaljournalssrg.org Page 215.
15.	3D Object Recognition Using Convolutional Neural Networks with Transfer Learning Between Input Channels Luís A. Alexandre
16.	B. Yang, J. Yan, Z. Lei, and S. Z. Li, “Aggregate channel features for multi-view face detection,” in IJCB, 2014.
17.	X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun, “3d object proposals for accurate object class detection,” in NIPS, 2015.
18.	K. Kang, H. Li, T. Xiao, W. Ouyang, J. Yan, X. Liu, and X. Wang, “Object detection in videos with tubelet proposal networks,” in CVPR, 2017.
19.	S. Paisitkriangkrai, C. Shen, and A. van den Hengel, “Pedestrian detection with spatially pooled features and structured ensemble learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, pp. 1243–1257, 2016.
20.	W. Byeon, T. M. Breuel, F. Raue, and M. Liwicki, “Scene labeling with lstm recurrent neural networks,” in CVPR, 2015

Cites this article as

[Mr. Utsab Mukherjee, Prof. Samir Kumar Bandyopadhyay* (2020), Detection of Human Behaviour by Object Recognition using Deep Learning: A Review, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-8, Issue-2, Page No-16-19], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

Mr. Utsab Mukherjee

Assistant Professor, Department of Computer Science, Bhawanipur Education Society College, West Bengal, India.

Download Full Paper

Download PDF

No. of Downloads: 50 | No. of Views: 1107

EEG-Based Multi-Class Emotion Recognition using Hybrid LSTM Approach

Md Momenul Haque, Subrata Kumer Paul, Rakhi Rani Paul, Mursheda Nusrat Della, Md. Kamrul Islam, Sultan Fahim.

May 2023 - Vol 11, Issue 3
Investigation of Impact of Temperature on Proposed Device with Reference to DIEL CYCLE.

Deepa Sonal, Khushboo Mishra, Mohit Kumar Mishra, Shailesh Kumar Shrivastava, Binay Kumar Mishra.

January 2023 - Vol 11, Issue 1
Development of Android-Based Mobile Application Using Gyroscope Sensor

Pragati Srivastava, Sushil Chandra, Rajeev Sonkar, Sanghmitra, Ayushi.

November 2022 - Vol 10, Issue 6

IJIRCST

Detection of Human Behaviour by Object Recognition using Deep Learning: A Review

Citations

Download Full Paper PDF

Total View 1107

Total Download 50