SUSPICIOUS HUMAN ACTIVITY RECOGNITION  AND ALARMING SYSTEM USING CNN AND  LSTM ALGORITHM

K.V Sai Likhita; Leela S; Deepak Kumar; A Abhiram

SUSPICIOUS HUMAN ACTIVITY RECOGNITION AND ALARMING SYSTEM USING CNN AND LSTM ALGORITHM

April 2022

Vol-8, Issue-2

Paper ID: 16407

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Science Engineering

Keywords

Human activity recognition · Adaptive video compression · Vision-based human activity recognition · Anomaly detection

Abstract

Traditional pattern recognition systems have made significant improvement in recent years. With deep learning methods' growing popularity and success, utilising these approaches to understand human behaviours in mobile and wearable computing settings has gotten a lot of interest. A deep neural network that blends convolutional layers with long short-term memory (LSTM) is proposed in this research. With a few model parameters, this model could automatically extract activity features and classify them. The LSTM is a recurrent neural network (RNN) variation that is better suited to handling temporal sequences. The raw data acquired by the sensors was fed into a two-layer LSTM followed by convolutional layers in the proposed architecture. It can not only extract activity features adaptively, but it also has less parameters and a higher accuracy. The CNN classifier, which should be used alone, and the LSTM models, which should be used in series and with the feed data, should be used together.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	K.V Sai Likhita	Dayananda Sagar academy of technology and management
2	Leela S	Dayananda Sagar academy of technology and management
3	Deepak Kumar	Dayananda Sagar academy of technology and management
4	A Abhiram	Dayananda Sagar academy of technology and management

How to Cite

Use the following formats to cite this article in your research.

APA Style

Likhita, K.V Sai, S, Leela, Kumar, Deepak, & Abhiram, A (2022). SUSPICIOUS HUMAN ACTIVITY RECOGNITION AND ALARMING SYSTEM USING CNN AND LSTM ALGORITHM. International Journal of Advance Research and Innovative Ideas In Education, 8(2), 1677-1682.

MLA Style

Likhita, K.V Sai, et al. "SUSPICIOUS HUMAN ACTIVITY RECOGNITION AND ALARMING SYSTEM USING CNN AND LSTM ALGORITHM." International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 2, 2022, pp. 1677-1682.

IEEE Style

K.V Sai Likhita, Leela S, Deepak Kumar, and A Abhiram, "SUSPICIOUS HUMAN ACTIVITY RECOGNITION AND ALARMING SYSTEM USING CNN AND LSTM ALGORITHM," International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 2, pp. 1677-1682, 2022.

Vancouver Style

Likhita K.V Sai, S Leela, Kumar Deepak, Abhiram A. SUSPICIOUS HUMAN ACTIVITY RECOGNITION AND ALARMING SYSTEM USING CNN AND LSTM ALGORITHM. International Journal of Advance Research and Innovative Ideas In Education. 2022;8(2):1677-1682.

Harvard Style

Likhita, K.V Sai, S, Leela, Kumar, Deepak, & Abhiram, A (2022) 'SUSPICIOUS HUMAN ACTIVITY RECOGNITION AND ALARMING SYSTEM USING CNN AND LSTM ALGORITHM', International Journal of Advance Research and Innovative Ideas In Education, 8(2), pp. 1677-1682.

Chicago Style

Likhita, K.V Sai, et al. "SUSPICIOUS HUMAN ACTIVITY RECOGNITION AND ALARMING SYSTEM USING CNN AND LSTM ALGORITHM." International Journal of Advance Research and Innovative Ideas In Education 8, no. 2 (2022): 1677-1682.

Turabian Style

Likhita, K.V Sai, et al. "SUSPICIOUS HUMAN ACTIVITY RECOGNITION AND ALARMING SYSTEM USING CNN AND LSTM ALGORITHM." International Journal of Advance Research and Innovative Ideas In Education 8, no. 2 (2022): 1677-1682.