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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Suspicious Human Activity detection using AI and ML |
| Authors Name: | Meghana C , Monika Kadam N , Preethi N , Priyanka P , Pratibha S |
| Download E-Certificate: | Download |
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IJNRD_220103
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| Published Paper Id: | IJNRD2404829 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | With the rise in anti-social activities, the need for robust security measures has become paramount. Closed-Circuit Television (CCTV) systems are commonly deployed for continuous surveillance, but the manual monitoring of extensive footage is impractical. This paper proposes an innovative approach by integrating YOLOv3 and Mobile LSTM into surveillance systems to efficiently identify abnormal activities with precision and speed. YOLOv3's object detection capabilities are leveraged to enhance the system's ability to detect concerning behaviors such as aggression, unauthorized access, and erratic movements. Additionally, Mobile LSTM is introduced for its proficiency in sequential data processing, enabling precise temporal analysis within video streams. By combining YOLOv3 and Mobile LSTM, the proposed model effectively captures both spatial and temporal information, enhancing the system's overall surveillance capabilities. This integration not only improves the accuracy of abnormal activity detection but also expedites decision-making processes, thus addressing the challenges posed by manual monitoring and bolstering security measures. |
| Keywords: | Artificial intelligence, YOLOv3 model, Mobile LSTM, video surveillance, abnormal activity detection |
| Cite Article: | "Suspicious Human Activity detection using AI and ML", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.i226-i229, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404829.pdf |
| Downloads: | 00042 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404829 Registration ID: 220103 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: i226-i229 Country: SHIMOGA, Karnataka, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404829 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404829 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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