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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68177
Volume:
10 |
10 |
Oct 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science
Patkar-Varde College
Advanced video monitoring systems may include many cameras, sources of videos, AI models, and the notification systems. Working with them separately may complicate and prolong the process of monitoring. VisionNext is the AI platform for video monitoring that integrates all these processes in one system. It allows its users to connect multiple live video sources via RTSP, RTMP, and mobile/OBS streaming and analyzes them with the help of Vision Language Models (VLMs). There are multiple standard cases of detection in VisionNext, which include fire detection, PPE detection, intrusion detection, and safety monitoring. Users can also configure their own detection requests via custom prompts. YOLO and SAM 3 models can be used for solving some specific computer vision problems such as object detection, segmentation, and tracking. Once any configured event occurs, the platform records such information as camera source, type of the event, time stamp, and image frame, and then sends email and phone call notifications to the necessary users. Also, there is role-based access control to the cameras, events, and other platform features. VisionNext platform is developed with FastAPI, MongoDB, Redis, and Docker for backend services, database, processing, and deployment.
KEYWORDS
VisionNext was introduced in this work, and it represents a system that integrates video streaming, detection based on artificial intelligence, event management, and notifications. The proposed platform is integrated with RTSP, RTMP, mobile, and VLM-based video streams and computer vision models such as YOLO and SAM 3. Since VisionNext has not yet been completely realized, we have no test results at this point. Therefore, we do not make any statements about its detection precision and performance. Instead, we provide the system architecture, implementation guidelines and evaluation scheme to assess the effectiveness of the system on controlled video data sets. We consider it a solid foundation for an AI-based video surveillance system and further evaluations.
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Publication Date
10/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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