International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Based Real-Time Multi-Class Abnormal Activity Detection using Slow-Fast 3D CNN for Smart Surveillance Systems |
|---|---|
| ABSTRACT | Smart surveillance systems play a growing role in keeping public, commercial, and institutional spaces safe. Traditional monitoring depends on people watching video feeds, which can be inefficient and slow to respond. This research introduces an AI-powered real-time surveillance system that can detect abnormal activities like violence, robbery, fighting, accidents, and unusual object movement in live video. The system uses a Slow-Fast 3D Convolutional Neural Network (SF-3D CNN) to capture both spatial and motion features. By analyzing video at different frame rates, the model can tell abnormal from normal behavior in places like colleges, hotels, public areas, and smart cities. When it detects suspicious events, the system automatically alerts the right authorities. Tests show the system can classify multiple types of abnormal activity accurately and efficiently, making it practical for real-world use. |
| AUTHOR | OM ANERAO, SANDEEP DHARURKAR, AJINKYA JADHAV, VARDHAN JADHAV, PRIYANKA JADHAV Student, Department of Computer Engineering, RMD Sinhgad School of Engineering, Pune, India Assistant Professor, Department of Computer Engineering, RMD Sinhgad School of Engineering, Pune, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402027 |
| pdf/27_AI-Based Real-Time Multi-Class Abnormal Activity Detection using Slow-Fast 3D CNN for Smart Surveillance Systems.pdf | |
| KEYWORDS | |
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