Suspicious Human Activity detection using AI and ML

Registration ID: IJNRD_220103

Published ID: IJNRD2404829

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Meghana C, Monika Kadam N, Preethi N, Priyanka P, Pratibha S

Artificial intelligence, YOLOv3 model, Mobile LSTM, video surveillance, abnormal activity detection

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.

Published Paper   E-Certificate

Meghana C, Monika Kadam N, Preethi N, Priyanka P, & Pratibha S (April-2024). Suspicious Human Activity detection using AI and ML. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), i226-i229. https://ijnrd.org/papers/IJNRD2404829.pdf

Volume 9 Issue 4, April-2024

Pages : i226-i229

Paper Reg. ID: IJNRD_220103

Published Paper Id: IJNRD2404829

Downloads: 000122253

Research Area: Computer Engineering 

Author Type: Indian Author

Country: SHIMOGA, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404829.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404829

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