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Precise Human Activity Recognition using Convolutional Neural Network and Deep Learning Models

Harshavardhan Patil1 , Priti Malkhede2 , Shreyash Madake3 , Ashutosh Kokate4 , Yash Bhandure5

  1. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.
  2. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.
  3. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.
  4. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.
  5. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.

Section:Research Paper, Product Type: Journal Paper
Volume-12 , Issue-7 , Page no. 24-32, Jul-2024

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v12i7.2432

Online published on Jul 31, 2024

Copyright © Harshavardhan Patil, Priti Malkhede, Shreyash Madake, Ashutosh Kokate, Yash Bhandure . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Harshavardhan Patil, Priti Malkhede, Shreyash Madake, Ashutosh Kokate, Yash Bhandure, “Precise Human Activity Recognition using Convolutional Neural Network and Deep Learning Models,” International Journal of Computer Sciences and Engineering, Vol.12, Issue.7, pp.24-32, 2024.

MLA Style Citation: Harshavardhan Patil, Priti Malkhede, Shreyash Madake, Ashutosh Kokate, Yash Bhandure "Precise Human Activity Recognition using Convolutional Neural Network and Deep Learning Models." International Journal of Computer Sciences and Engineering 12.7 (2024): 24-32.

APA Style Citation: Harshavardhan Patil, Priti Malkhede, Shreyash Madake, Ashutosh Kokate, Yash Bhandure, (2024). Precise Human Activity Recognition using Convolutional Neural Network and Deep Learning Models. International Journal of Computer Sciences and Engineering, 12(7), 24-32.

BibTex Style Citation:
@article{Patil_2024,
author = {Harshavardhan Patil, Priti Malkhede, Shreyash Madake, Ashutosh Kokate, Yash Bhandure},
title = {Precise Human Activity Recognition using Convolutional Neural Network and Deep Learning Models},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2024},
volume = {12},
Issue = {7},
month = {7},
year = {2024},
issn = {2347-2693},
pages = {24-32},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5707},
doi = {https://doi.org/10.26438/ijcse/v12i7.2432}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v12i7.2432}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5707
TI - Precise Human Activity Recognition using Convolutional Neural Network and Deep Learning Models
T2 - International Journal of Computer Sciences and Engineering
AU - Harshavardhan Patil, Priti Malkhede, Shreyash Madake, Ashutosh Kokate, Yash Bhandure
PY - 2024
DA - 2024/07/31
PB - IJCSE, Indore, INDIA
SP - 24-32
IS - 7
VL - 12
SN - 2347-2693
ER -

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Abstract

Human Activity Recognition (HAR) plays a pivotal role in various domains, ranging from healthcare to surveillance and robotics. This paper offers a comprehensive detail of Convolutional Neural Network (CNN)-based methodologies in HAR, emphasizing their efficiency in accurately recognizing human activities from video data. We used the UCF50 dataset, which contains videos of 50 different human activities, making it a suitable benchmark for evaluating CNN-based HAR models. The study investigates the utilization of CNNs for feature extraction and classification in HAR, focusing on techniques such as frame extraction, data preprocessing, and model architectures. Detailed analysis of convolutional layers, pooling layers, and activation functions within CNNs showcases their ability to capture intricate spatial and temporal features. The research also delves into the benefits of data augmentation and normalization in enhancing model performance and generalization. The findings highlight the significant advantages of CNNs in capturing spatial information and improving accuracy in HAR tasks, making them highly effective for real-world applications across various domains.

Key-Words / Index Term

Convolutional Neural Network, Human Activity Recognition, UCF50 Dataset, Data Preprocessing, Frame Extraction, Model Architecture, Feature Extraction

References

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