An Intelligent System for Recognizing the Human Activity using Improved Convolutional Neural Network (I-CNN) Model
Keywords:
Human activity, Improved CNN, deep learning, activity recognition and artificial intelligenceAbstract
In account of the many practical uses of computer vision, researchers have made human behavior detection in videos a top priority. It covers a broad range of topics, such as video surveillance, behavior analysis, sports analysis, e-health, patient monitoring, assisted daily living, and much more. Many researchers have been proposed methods that rely on vision to identify human activity. Researchers will need to address problems of variations in human activity detection, class comparison between pictures, and temporal variation in order to construct an effective vision-based human behavior detection system.
This paper proposes an improved Convolutional Neural Network (I-CNN) to boost human behavior detection performance and address these issues. A dilated convolution filter widens the convolutional layer's receptive field to allow for the incorporation of more pertinent data. Additionally to the standard CNN model, it boosts CNN performance and reduces the calculation time. We've compiled KTH publicly available benchmark dataset, to test the effectiveness of suggested process. We show experimental findings showing that our suggested model outperforms the standard CNN model on the aforementioned dataset, achieving 96.85%.
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J. Aggarwal and M. Ryoo, “Human activity analysis: A review,” ACM Comput. Surv., vol. 43, pp. 16:1–16:43, Apr. 2011.
Danafar, S. and Gheissari, N. Action recognition for surveillance applications using optic flow and SVM. In Asian Conference on Computer Vision, Vol. 6(2), pp. 457–466, 2007.
Gorelick, Lena, Moshe Blank, Eli Shechtman, Michal Irani, and Ronen Basri, "Actions as space-time shapes." IEEE transactions on pattern analysis and machine intelligence, Vol. 29, No. 12, pp. 2247-2253, 2007.
Ankur Agarwal and Bill Triggs, “Multilevel image coding with hyper features”, International Journal of Computer Vision, Vol. 78(1), pp. 15–27, 2008.
A. Gilbert, J. Illingworth, and R. Bowden, “Action recognition using mined hierarchical compound features”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33(5), pp. 883–897, 2011.
Dimitris Metaxas and Shaoting Zhang, "A review of motion analysis methods for human Nonverbal Communication Computing", Image and Vision Computing, vol. 31, pp. 421-433, 2013.
Hbali, Y., Hbali, S., Ballihi, L., & Sadgal, M., Skeleton‐based human behavior detection for elderly monitoring systems. IET Computer Vision, Vol. 12(1), pp. 16-26, 2018.
Theodorakopoulos, I., Kastaniotis, D., Economou, G., & Fotopoulos, S., Pose-based human action recognition via sparse representation in dissimilarity space. Journal of Visual Communication and Image Representation, Vol. 25(1), pp. 12-23, 2014.
Gaglio, S., Re, G. L., & Morana, M., Human behavior detection process using 3-D posture data. IEEE Transactions on Human-Machine Systems, Vol. 45(5), pp. 586-597, 2014.
S. R. Ke, H. Thuc, and Y. J. Lee, “A review on video-based human behavior detection ,” Computers, vol. 2, no. 2, pp. 88-131, Mar. 2013.
C. Dai, X. Liu, J. Lai, P. Li, and Han-Chieh Chao, “Human behaviour deep recognition architecture for smart city applications in 5G environment” IEEE Netw., DOI:10.1109/MNET.
1800310, 2012.
X. Zhen, and L. Shao, “Action recognition via spatio-temporal local features: A comprehensive study,” Image Vis. Comput., vol. 50, pp. 1-13, Jun. 2016.
H. Kwon, Y. Kim, J. S. Lee, and M. Cho, “First person action recognition via two-stream convnet with long-term fusion pooling,” Pattern Recogn. Lett., vol. 112, pp. 161-167, Sep. 2018.
B. Saghafi and D. Rajan, “Human action recognition using pose-based discriminant embedding,” Signal Process-Image Commun., vol. 27, no.1, pp. 96-111, Jan. 2012.
A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, P. Martinez-Gonzalez, and J. Garcia-Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Appl. Soft Comput., vol. 70, pp. 41-65, Sep. 2018.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw., vol. 61, pp. 85-117, Jan. 2015.
S. Ji,W. Xu, M. Yang, and K.Yu, “3D Convolutional Neural Networks for Human Action Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 221-231, Mar. 2012.
K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos, ” in Proc. 2014 IEEE Int. Conf. Neural. Inf. Process. Syst., pp. 568-576, Kuching, Malaysia, Nov. 2014.
C. Feichtenhpfer, A. Pinz, and A. Zisserman, “Convolutional twostream network fusion for video action recognition,” in Proc. 2016 Int. Conf. Comput. Vis. Pattern Recognit., pp. 1933-1941, Las Vegas, USA, Jun. 2016.
Harikrishna. B, Ramakrishnam Raju S.V.S, Sanjay Kumar Suman, Bhagyalakshmi L, “Artificial Intelligence Framework for Rice Blast Disease Detection and Classification Using Recurrent Neural Networks”, IET-IEEE 8th International Conference on Computing in Engineering and Technology (ICCET 2023), Patna 14-15, July 2023. doi: https://doi.org/10.1049/icp.2023.1461.
Ramakrishnam Raju S.V.S, Harikrishna. B, Bhagyalakshmi. L, Sanjay Kumar Suman, “Anomaly detection using self-supervised label generator with vertical disparity maps”, IET-IEEE 8th International Conference on Computing in Engineering and Technology (ICCET 2023), Patna 14-15, July 2023. doi: https://doi.org/10.1049/icp.2023.1461.
M.D. Javeeda, Regonda Nagarajub, Raja Chandrasekaran, Govinda Rajulu, Praveen Tumuluru, M. Ramesh, Sanjay Kumar Suman, and Rajeev Shrivastava, “Brain tumor segmentation and classification with hybrid clustering, probabilistic neural networks”, Journal of Intelligent & Fuzzy Systems, IoS Press, vol. 45, issue 4, pp. 6485-6500, 2023. DOI: http://doi.org/10.3233/JIFS-232493.
Himanshu Shekhar, Chandra Bhushan Mahato, Sanjay Kumar Suman, Satyanand Singh, L. Bhagyalakshmi, Mahendra Prasad Sharma, B. Laxmi Kantha, Helan Vidhya T, Siva Kumar Agraharam & A. Rajaram, “Demand Side Control for Energy Saving in Renewable Energy Resources Using Deep Learning Optimization,” Electric Power Components and Systems, Taylor & Francis, vol. 51, issue 19, pp. 2397-2413, 2023. DOI: http://doi.
org/10.1080/15325008.2023.2246463.
Sanjay Kumar Suman, Himanshu Shekhar, Chandra Bhushan Mahto, D. Gururaj, L. Bhagyalakshmi, and P. Santosh Kumar Patra, “Sign Language Interpreter”, Springer Nature Singapore, Advances in Cognitive Science and Communications, Cognitive Science and Technology, pp. 1021-1031, 2023, https://doi.org/10.
/978-981-19-8086-2_96
Rajesh Tiwari, Rajeev Shrivastava, Santosh Kumar Vishwakarma, Sanjay Kumar Suman, and Sheo Kumar, “InterCloud: Utility-Oriented Federation of Cloud Computing Environments Through Different Application Services”, Springer Nature Singapore, Advances in Cognitive Science and Communications, Cognitive Science and Technology, pp. 83-89, 2023. https://doi.org/10.
/978-981-19-8086-2_8
Rajeev Shrivastava, Mallika Jain, Santosh Kumar Vishwakarma, L. Bhagyalakshmi, and Rajesh Tiwari, “Cross-Cultural Translation Studies in the Context of Artificial Intelligence: Challenges and Strategies”, Springer Nature Singapore, Advances in Cognitive Science and Communications, Cognitive Science and Technology, pp. 91-98, 2023. https://doi.org/10.1007/978-981-19-8086-2_9
Abhishek Kumar, Sanjay Kumar Suman, L. Bhagyalakshmi, and Anil Kumar Sahu, “IoT and Cloud Network Based Water Quality Monitoring System Using IFTTT Framework”, Springer Nature, Sustainable Technology and Advanced Computing in Electrical Engineering, Lecture Notes in Electrical Engineering 939, pp. 23-32, 2023. https://doi.org/10.1007/978-981-19-4364-5_3
Sanjay Kumar Suman, Dhananjay Kumar and L. Bhagyalakshmi, “SINR pricing in non- cooperative power control game for wireless ad hoc network”, KSII Transactions on Internet and Information Systems, KSII TIIS, vol. 8, no. 7, pp. 2281-2301, 2014. https://dio.
org/10.3837/tiis.2014.07.005
L. Bhagyalakshmi, Sanjay Kumar Suman, Sujeetha Devi, “Joint Routing and Resource Allocation for Cluster Based Isolated Nodes in Cognitive Radio Wireless Sensor Networks”, Wireless Personal Communication, Springer, vol. 114, issue 4, pp. 3477- 3488, Oct. 2020. https://doi.org/10.1007/s11277-020-07543-4
K. Mahalakshmi, K. Kousalya, Himanshu Shekhar, Aby K. Thomas, L. Bhagyalakshmi, Sanjay Kumar Suman et. al., “Public Auditing Scheme for Integrity Verification in Distributed Cloud Storage System”, Scientific Programming, Hindawi, vol. 2021, Article ID 8533995, Dec. 2021. .https://doi.org/10.1155/2021/
Sanjay Kumar Suman et. al., Detection and prediction of HMS from drinking water by analysing the adsorbents from residuals using deep learning, Hindawi (SAGE Journal) Adsorption Science & Technology, vol. 2022, Article id 3265366, March 2022. https://doi.org/10.1155/2022/3265366
Bhagyalakshmi and K. Murugan, “Avoiding Energy Holes Problem using Load Balancing Approach in Wireless Sensor Network”, KSII Transaction on Internet and Information Systems, vol. 8 , no. 5, pp. 1618- 1637, 2014. https://dio.org/10.3837/
tiis.2014.05.007.
Satyanand Singh, Sajai Vir Singh, Dinesh Yadav, Sanjay Kumar Suman, Bhagyalakshmi Lakshminarayanan, Ghanshyam Singh, “Discrete interferences optimum beamformer in correlated signal and interfering noise, International Journal of Electrical and Computer Engineering, vol. 12, no. 2, pp. 1732-1743, April 2022. http://doi.org/10.11591/ijece.v12i2.pp1732-1743.
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