Unified Physical Threat Monitoring System Aided by Virtual Building
Simulation
- URL: http://arxiv.org/abs/2203.00789v1
- Date: Tue, 1 Mar 2022 23:28:46 GMT
- Title: Unified Physical Threat Monitoring System Aided by Virtual Building
Simulation
- Authors: Zenjie Li and Barry Norton
- Abstract summary: A physical threat monitoring solution unifying the floorplan, cameras, and sensors for smart buildings has been set up in our study.
A physical threat monitoring system typically needs to address complex and even destructive incidents, such as fire, which is unrealistic to simulate in real life.
This study uses the Unreal Engine to simulate some typical suspicious and intrusion scenes with photorealistic qualities in the context of a virtual building.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With increasing physical threats in recent years targeted at critical
infrastructures, it is crucial to establish a reliable threat monitoring system
integrating video surveillance and digital sensors based on cutting-edge
technologies. A physical threat monitoring solution unifying the floorplan,
cameras, and sensors for smart buildings has been set up in our study. Computer
vision and deep learning models are used for video streams analysis. When a
threat is detected by a rule engine based on the real-time analysis results
combining with feedback from related digital sensors, an alert is sent to the
Video Management System so that human operators can take further action. A
physical threat monitoring system typically needs to address complex and even
destructive incidents, such as fire, which is unrealistic to simulate in real
life. Restrictions imposed during the Covid-19 pandemic and privacy concerns
have added to the challenges. Our study utilises the Unreal Engine to simulate
some typical suspicious and intrusion scenes with photorealistic qualities in
the context of a virtual building. Add-on programs are implemented to transfer
the video stream from virtual PTZ cameras to the Milestone Video Management
System and enable users to control those cameras from the graphic client
application. Virtual sensors such as fire alarms, temperature sensors and door
access controls are implemented similarly, fulfilling the same programmatic VMS
interface as real-life sensors. Thanks to this simulation system's
extensibility and repeatability, we have consolidated this unified physical
threat monitoring system and verified its effectiveness and user-friendliness.
Both the simulated Unreal scenes and the software add-ons developed during this
study are highly modulated and thereby are ready for reuse in future projects
in this area.
Related papers
- E-Motion: Future Motion Simulation via Event Sequence Diffusion [86.80533612211502]
Event-based sensors may potentially offer a unique opportunity to predict future motion with a level of detail and precision previously unachievable.
We propose to integrate the strong learning capacity of the video diffusion model with the rich motion information of an event camera as a motion simulation framework.
Our findings suggest a promising direction for future research in enhancing the interpretative power and predictive accuracy of computer vision systems.
arXiv Detail & Related papers (2024-10-11T09:19:23Z) - Modeling Electromagnetic Signal Injection Attacks on Camera-based Smart Systems: Applications and Mitigation [18.909937495767313]
electromagnetic waves pose a threat to safety- or security-critical systems.
Such attacks enable attackers to manipulate the images remotely, leading to incorrect AI decisions.
We present a pilot study on adversarial training to improve their robustness against attacks.
arXiv Detail & Related papers (2024-08-09T15:33:28Z) - Investigation of Multi-stage Attack and Defense Simulation for Data Synthesis [2.479074862022315]
This study proposes a model for generating synthetic data of multi-stage cyber attacks in the power grid.
It uses attack trees to model the attacker's sequence of steps and a game-theoretic approach to incorporate the defender's actions.
arXiv Detail & Related papers (2023-12-21T09:54:18Z) - UniSim: A Neural Closed-Loop Sensor Simulator [76.79818601389992]
We present UniSim, a neural sensor simulator that takes a single recorded log captured by a sensor-equipped vehicle.
UniSim builds neural feature grids to reconstruct both the static background and dynamic actors in the scene.
We incorporate learnable priors for dynamic objects, and leverage a convolutional network to complete unseen regions.
arXiv Detail & Related papers (2023-08-03T17:56:06Z) - Understanding Policy and Technical Aspects of AI-Enabled Smart Video
Surveillance to Address Public Safety [2.2427353485837545]
This paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance.
We propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications.
arXiv Detail & Related papers (2023-02-08T19:54:35Z) - Drone Detection and Tracking in Real-Time by Fusion of Different Sensing
Modalities [66.4525391417921]
We design and evaluate a multi-sensor drone detection system.
Our solution integrates a fish-eye camera as well to monitor a wider part of the sky and steer the other cameras towards objects of interest.
The thermal camera is shown to be a feasible solution as good as the video camera, even if the camera employed here has a lower resolution.
arXiv Detail & Related papers (2022-07-05T10:00:58Z) - VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and
Policy Learning for Autonomous Vehicles [131.2240621036954]
We present VISTA, an open source, data-driven simulator that integrates multiple types of sensors for autonomous vehicles.
Using high fidelity, real-world datasets, VISTA represents and simulates RGB cameras, 3D LiDAR, and event-based cameras.
We demonstrate the ability to train and test perception-to-control policies across each of the sensor types and showcase the power of this approach via deployment on a full scale autonomous vehicle.
arXiv Detail & Related papers (2021-11-23T18:58:10Z) - Anomaly Detection in Residential Video Surveillance on Edge Devices in
IoT Framework [1.5293427903448025]
We propose anomaly detection for intelligent surveillance using CPU-only edge devices.
A modular framework to capture object-level inferences and tracking is developed.
Experiments indicate the proposed method is feasible and achieves satisfactory results in real-life scenarios.
arXiv Detail & Related papers (2021-07-10T05:52:15Z) - Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for
Online Collision Avoidance [95.86944752753564]
We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties.
Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates.
The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
arXiv Detail & Related papers (2020-07-28T07:34:30Z) - Towards robust sensing for Autonomous Vehicles: An adversarial
perspective [82.83630604517249]
It is of primary importance that the resulting decisions are robust to perturbations.
Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements.
A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
arXiv Detail & Related papers (2020-07-14T05:25:15Z)
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.