City Sentinel: A Unified AI-Based Smart Surveillance Framework for Real-Time Multi-Threat Detection Using Deep Learning

• URL: http://arxiv.org/abs/2608.08887v1
• Date: Sun, 09 Aug 2026 19:59:31 GMT
• Title: City Sentinel: A Unified AI-Based Smart Surveillance Framework for Real-Time Multi-Threat Detection Using Deep Learning
• Abstract summary: City Sentinel is a unified AI-based surveillance framework that integrates six detection capabilities into one scalable platform.<n>System achieves median end-to-end latency of 743 ms and supports four concurrent RTSP streams.<n>It achieves a 91.2% face-match rate, 85.7% plate-reading accuracy, and mAP@0.5 scores of 0.846 to 0.889 across the fire, knife, and weapon detection modules.
• Score: 0.06999740786886534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid urbanization has increased the need for surveillance systems that can monitor multiple public safety risks at the same time. Traditional systems often use separate solutions for facial recognition, vehicle identification, fire detection, and behavioral analysis, resulting in fragmented infrastructure and multiple interfaces for operators to manage. This paper presents City Sentinel, a unified AI-based surveillance framework that integrates six detection capabilities into one scalable platform: facial recognition, automatic number plate recognition (ANPR), fire and smoke detection, weapon and knife detection, violence detection, and road accident detection. The system combines a Next.js operator dashboard, FastAPI backend, cloud-based PostgreSQL event storage, InsightFace and YOLOv8 vision models, and EasyOCR for plate recognition. Camera streams are processed through dedicated inference workers using RTSP. On a workstation equipped with an NVIDIA RTX 3060 GPU, the system achieves a median end-to-end latency of 743 ms and supports four concurrent RTSP streams within a two-second latency limit. It achieves a 91.2% face-match rate, 85.7% plate-reading accuracy, and mAP@0.5 scores of 0.846 to 0.889 across the fire, knife, and weapon detection modules. In user-acceptance testing, operators could enroll a new identity in under one minute and identify a flagged person from live footage in an average of 12 seconds. The results demonstrate that a modular, open-source, multi-model architecture can provide broad surveillance coverage, cloud-based auditability, and flexibility for adding new detection capabilities while maintaining practical real-time performance.

Related papers

• Autonomous Cyber Defense: Real-Time Attack Detection and Mitigation in Software-Defined Networks Using Machine Learning [78.29352305480285]
  In recent intrusions, the interval between initial access and the first lateral movement has been observed to be as short as 27 seconds.<n>This paper presents a closed-loop framework that detects and blocks attacks in software-defined networks without operator involvement.
  arXiv  Detail & Related papers  (2026-08-22T18:38:39Z)
• A Multi-Task Deep Learning Framework for Real-Time Intelligent Video Surveillance with Temporal Event Validation [0.0]
  This paper presents a unified multi-task deep learning framework that simultaneously performs face recognition, license plate recognition, weapon detection, fire and smoke detection, and human action recognition.<n>To improve robustness in continuous video, all detection modules are integrated into a temporal event-validation architecture.<n>Results show that the framework maintains real-time operation with a per-frame latency below 100 ms on commodity hardware.
  arXiv  Detail & Related papers  (2026-07-03T09:20:58Z)
• Real-Time Multi-Modal Embedded Vision Framework for Object Detection Facial Emotion Recognition and Biometric Identification on Low-Power Edge Platforms [0.44219509596259216]
  We present a real-time multi-modal vision framework that integrates object detection, owner-specific face recognition, and emotion detection into a unified pipeline deployed on a Raspberry Pi 5 edge platform.<n>Our work demonstrates that context-aware scheduling is the key to unlocking complex multi-modal AI on cost-effective edge hardware.
  arXiv  Detail & Related papers  (2026-01-17T09:06:47Z)
• Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection? [57.000348519630286]
  Recent advances in mobile edge computing have made it possible to offload-intensive object detection to edge servers equipped with high-accuracy neural networks.<n>This hybrid approach offers a promising solution but introduces a new challenge: deciding when to perform edge detection versus local tracking.<n>We propose the LTED-Ada in single-device setting, a deep reinforcement learning-based algorithm that adaptively selects between local tracking and edge detection.
  arXiv  Detail & Related papers  (2025-11-25T04:54:51Z)
• A Deep Learning-Based CCTV System for Automatic Smoking Detection in Fire Exit Zones [0.0]
  A real-time smoking detection system for CCTV surveillance of fire exit areas is proposed due to critical safety requirements.<n>A dataset contains 8,124 images from 20 different scenarios along with 2,708 raw samples demonstrating low-light areas.<n>The proposed model outperformed the others, achieving a recall of 78.90 percent and mAP at 50 of 83.70 percent.
  arXiv  Detail & Related papers  (2025-08-12T19:27:17Z)
• OpenRSD: Towards Open-prompts for Object Detection in Remote Sensing Images [45.40710102095654]
  We propose OpenRSD, a universal open-prompt RS object detection framework.<n>OpenRSD supports multimodal prompts and integrates multi-task detection heads to balance accuracy and real-time requirements.<n>Compared to YOLO-World, OpenRSD exhibits an 8.7% higher average precision and achieves an inference speed of 20.8 FPS.
  arXiv  Detail & Related papers  (2025-03-08T10:08:46Z)
• Real-Time Pedestrian Detection on IoT Edge Devices: A Lightweight Deep Learning Approach [1.4732811715354455]
  This research explores implementing a lightweight deep learning model on Artificial Intelligence of Things (AIoT) edge devices. An optimized You Only Look Once (YOLO) based DL model is deployed for real-time pedestrian detection. The simulation results demonstrate that the optimized YOLO model can achieve real-time pedestrian detection, with a fast inference speed of 147 milliseconds, a frame rate of 2.3 frames per second, and an accuracy of 78%.
  arXiv  Detail & Related papers  (2024-09-24T04:48:41Z)
• A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs [13.581341206178525]
  Intrusion detection and mitigation approaches have shown promising results in detecting multiple attack vectors in Controller Area Network (CAN) We present a lightweight multi-attack quantised machine learning model that is deployed using Xilinx's Deep Learning Processing Unit IP on a Zynq Ultrascale+ (XCZU3EG) FPGA. The model detects denial of service and fuzzing attacks with an accuracy of above 99 % and a false positive rate of 0.07%, which are comparable to the state-of-the-art techniques in the literature.
  arXiv  Detail & Related papers  (2024-01-19T13:39:05Z)
• ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection [36.05807963935458]
  We leverage a vision transformer architecture for joint spoof detection and matching. We report competitive results with state-of-the-art (SOTA) models for both a sequential system and a unified architecture. We demonstrate the capability of our unified model to achieve an average integrated matching (IM) accuracy of 98.87% across LivDet 2013 and 2015 CrossMatch sensors.
  arXiv  Detail & Related papers  (2023-05-12T16:51:14Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds [53.07042574352251]
  We design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework.
  arXiv  Detail & Related papers  (2020-08-12T12:03:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.