Machine learning- powered intelligent CCTV for crime detection and proactive security

Simple item page
dc.contributor.authorJamkhandikar, Irfan
dc.contributor.authorBhosale, Mrunal [22CO02]
dc.contributor.authorBaditya, Deepak [22CO13]
dc.contributor.authorGaikwad, Sankalp [22CO17]
dc.contributor.authorMadar, Adnan [22CO25]
dc.date.accessioned2026-08-20T09:32:25Z
dc.date.available2026-08-20T09:32:25Z
dc.date.issued2026-06
dc.description.abstractTraditional CCTV systems depend on continuous human monitoring, often leading to delayed threat recognition and ineffective responses. This project presents a Smart CCTV Application Extention that transforms conventional surveillance into an intelligent, real-time monitoring system using artificial intelligence and machine learning. The system integrates advanced computer vision and deep learning models YOLO, CNN, and Lip- Net to automatically detect threats such as weapon presence, unauthorized intrusion, and shoplifting, while also interpreting lip movements to identify potentially harmful verbal cues. Once suspicious activity is detected, automated alerts are generated to notify relevant authorities for immediate action. Designed for compatibility with existing CCTV infrastructure, the system offers a cost-effective and scalable upgrade requiring no hardware modifications. Experimental evaluations demonstrate high detection accuracy, efficient real-time processing, and adaptability across diverse environments. This approach enhances situational awareness, minimizes human dependency, and represents a significant step toward autonomous and proactive security systems suitable for both public and private surveillance applications.
dc.identifier.urihttp://aiktc.ndl.gov.in/handle/123456789/4570
dc.language.isoen_US
dc.publisherAIKTC
dc.titleMachine learning- powered intelligent CCTV for crime detection and proactive security
dc.typeProject Report
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Grp_09_ ML based Smart CCTV Survillance and Alert System.pdf
Size:
2.51 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Project Reports - Computer Engineering