A SYSTEM & METHOD FOR DETECTING FIRE AND SMOKE USING CCTV CAMERAS

Abstract

The invention provides a method for detecting fire and smoke using CCTV cameras integrated with machine learning algorithms. The system captures real-time video and processes it to identify visual cues indicative of fire or smoke, employing convolutional neural networks (CNNs) for accurate detection. The system automatically generates alerts and sends them to a central monitoring station or directly to emergency authorities, enabling quick responses. It can also activate safety mechanisms like sprinklers or fire containment systems. This method uses existing CCTV infrastructure, making it a cost-effective solution adaptable for residential, commercial, and industrial environments. It aims to enhance fire detection accuracy and reduce response times, ensuring comprehensive fire safety management.

Specification

Description:The present invention belongs to the field of fire safety and surveillance systems, specifically focusing on methods and technologies for detecting fire and smoke using CCTV (Closed-Circuit Television) cameras. It integrates advanced image processing and machine learning algorithms to provide an automated, real-time solution for fire hazard detection and alert generation. The invention is applicable in residential, commercial, industrial, and public settings, enhancing safety measures by leveraging existing surveillance infrastructure.
BACKGROUND OF THE INVENTION
Traditional fire detection systems, such as smoke alarms and fire detectors, are often limited in effectiveness, particularly in large or open spaces. These systems primarily rely on sensors to detect smoke particles or heat, which may not provide early warnings, especially in environments where smoke may disperse before reaching the detectors. Furthermore, existing fire detection systems require installation of dedicated hardware, which increases costs and complexity.
CCTV cameras, on the other hand, are commonly installed in various locations for security purposes. These cameras offer continuous visual monitoring but are generally not integrated with fire detection functionalities. The advent of digital image processing and machine learning has created an opportunity to enhance fire safety systems by utilizing these pre-existing cameras for fire and smoke detection. By leveraging the visual capabilities of CCTV systems, this invention aims to provide a cost-effective, efficient, and accurate solution for detecting fire and smoke, thereby reducing response times and minimizing potential damages.
OBJECTS OF THE INVENTION
An object of the present invention is to develop a system capable of detecting the presence of fire and smoke in real-time, ensuring early warnings and timely responses to minimize damage and loss.
Another object of the present invention is to integrate fire detection capabilities into existing CCTV camera systems, reducing the need for additional hardware and lowering implementation costs.
Yet another object of the present invention is to employ advanced machine learning and deep learning techniques, such as convolutional neural networks (CNNs), for accurately detecting fire and smoke patterns while minimizing false alarms.
Another object of the present invention is to create an automated alert mechanism that communicates with emergency services, central monitoring units, or other safety systems like sprinklers upon detection of fire hazards.
Another object of the present invention is to develop a scalable system that can be easily expanded by adding more cameras or processing units, and adaptable for different environments, including industrial sites, commercial complexes, residential buildings, and public areas.
Another object of the present invention is to enable the system to activate safety mechanisms, such as automatic sprinklers or fire suppression systems, upon confirmation of a fire event, thus preventing the spread of fire.
SUMMARY OF THE INVENTION
According to the present invention, a method for detecting fire and smoke using CCTV cameras, combining real-time video monitoring with machine learning algorithms to provide an accurate and reliable fire detection system. The method involves capturing video footage from CCTV cameras installed in designated areas and processing these images using deep learning techniques, specifically convolutional neural networks (CNNs), trained to recognize visual cues associated with fire and smoke.
Once fire or smoke is detected, the system generates alerts that are sent to a central monitoring unit or directly to emergency authorities, allowing for immediate action. The system can also trigger automatic safety mechanisms, such as activating sprinklers or closing fire containment doors. The invention utilizes existing CCTV infrastructure, making it a cost-effective solution suitable for environments ranging from residential to industrial settings.
The system is designed to be scalable and adaptable, allowing for easy expansion by adding more cameras and processing units as needed. Furthermore, it includes mechanisms for cross-verifying fire events by using multiple camera feeds, thereby improving detection accuracy and reducing false alarms. The method also supports integration with a user interface for real-time monitoring, allowing users to view live footage and receive alerts, ensuring comprehensive and efficient fire hazard management.
In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
DETAILED DESCRIPTION OF THE INVENTION
An embodiment of this invention, illustrating its features, will now be described in detail. The words "comprising," "having," "containing," and "including," and other forms thereof are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items.
The terms "first," "second," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another, and the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.
The proposed invention utilizes CCTV cameras integrated with advanced image processing and machine learning algorithms to detect fire and smoke accurately and promptly. The system operates in multiple stages, each involving a combination of hardware components, software algorithms, and communication protocols to ensure efficient and effective fire hazard detection. The following is a comprehensive breakdown of each stage:
System Setup and Integration:
CCTV Camera Installation: The system begins with the installation or utilization of existing CCTV cameras strategically positioned throughout the monitored area. These cameras cover crucial locations such as hallways, open spaces, and high-risk zones like kitchens, storage areas, or machinery rooms.
Connection to Central Processing Unit (CPU): Each camera is connected to a central processing unit (CPU) through either wired or wireless networks. The CPU is responsible for receiving the video feed in real-time, processing the data, and running the machine learning algorithms.
Software Integration: The system software, which includes the machine learning algorithms and image processing models, is installed on the CPU. The software integrates with the existing CCTV systems and is configured to process video feeds continuously without interrupting standard security operations.
Video Capture and Frame Analysis:
Real-Time Video Monitoring: The CCTV cameras continuously capture video footage and transmit it to the CPU. The system divides the video into individual frames for detailed analysis. Each frame is treated as a single image that the software evaluates for signs of fire or smoke.
Frame Sampling: To optimize the processing speed, the system uses a frame sampling technique where only a subset of frames per second is analyzed, ensuring real-time monitoring while minimizing computational load. The sampling rate is adjustable based on the environment and risk level; high-risk areas may have higher sampling rates to enhance accuracy.
Machine Learning Model Deployment:
Convolutional Neural Networks (CNNs): At the core of the invention is a deep learning model built using convolutional neural networks (CNNs). CNNs are well-suited for image analysis due to their ability to detect patterns and features within visual data. The model is trained on a large and diverse dataset of fire and smoke images under various conditions, including different angles, lighting scenarios, and environments (indoor and outdoor).
Training and Transfer Learning: The CNN model is further optimized using transfer learning techniques. By leveraging pre-trained models on similar image recognition tasks, the system adapts quickly and efficiently to the specific requirements of fire and smoke detection. The transfer learning approach allows the model to generalize better and recognize fire and smoke accurately, even when conditions such as low light or partial obstructions are present.
Image Processing and Feature Detection:
Feature Recognition: Each frame processed by the system is analyzed for specific visual features associated with fire and smoke. The model identifies elements such as flame shapes, smoke plumes, sudden changes in color (e.g., a shift to red or orange hues), or rapid alterations in brightness.
Temporal and Spatial Analysis: The system does not rely on individual frames alone; it performs temporal and spatial analysis, considering sequences of frames over time. This helps distinguish between genuine fire events and other phenomena that may appear similar in a single frame, such as sunlight reflections, fog, or shadows.
Dynamic Adjustment: The sensitivity of the feature detection process is dynamically adjustable. In areas prone to false positives (e.g., areas with frequent sunlight reflections), the system's sensitivity can be calibrated to avoid false alarms, ensuring reliable performance.
Verification through Multi-Camera Integration:
Cross-Verification: To reduce false alarms and enhance accuracy, the system includes a verification mechanism where multiple camera feeds are cross-checked. When a fire or smoke event is detected in one camera feed, the system automatically analyzes the footage from adjacent cameras covering the same or nearby areas.
Correlation and Confirmation: The cross-referenced footage is correlated to confirm the presence of fire or smoke. This multi-camera verification process significantly reduces the chances of false positives caused by environmental factors such as fog or light reflections, which might be mistaken for smoke in a single feed.
Automated Alert and Communication System:
Immediate Alert Generation: Once the system confirms the presence of fire or smoke, it triggers an automated alert. The alert system is designed to operate on multiple channels, including audio-visual alarms within the premises and notifications sent to a central monitoring station.
Communication with Emergency Authorities: The system is capable of transmitting alert information directly to fire departments or emergency response teams via SMS, email, or API integration with emergency dispatch systems. This direct communication ensures that response teams are notified immediately, minimizing response times.
Integration with Safety Mechanisms: In addition to alerting authorities, the system can be integrated with automated safety mechanisms such as fire suppression systems (e.g., sprinklers or gas-based extinguishing systems). Upon detection and confirmation of fire, the system can trigger these safety mechanisms to contain the fire before it spreads.
Scalability and Adaptability:
Scalability of the System: The invention is designed to be scalable, allowing for the addition of new CCTV cameras and processing units as the monitored area expands. The software architecture supports multiple processing units operating in parallel, enabling the system to cover larger spaces, such as factories, shopping malls, or residential complexes, without compromising processing speed or accuracy.
Adaptability for Different Environments: The system is adaptable to a variety of environments, including industrial settings where machinery might create visual interference, outdoor spaces with varying light conditions, and residential buildings with complex layouts. The software allows customization for each environment, including adjustments in frame sampling rates, alert protocols, and sensitivity settings.
User Interface and Real-Time Monitoring:
Central Monitoring Interface: The system includes a user interface accessible via a central monitoring station. The interface displays real-time video feeds, allowing security personnel or facility managers to monitor footage from all connected cameras simultaneously. The interface highlights areas where fire or smoke is detected, providing instant visual cues and information about the alert.
Mobile Application Integration: For greater accessibility, the system can be integrated with a mobile application, enabling users to receive notifications, view camera feeds, and manage alerts remotely. This feature is particularly useful for large facilities where the monitoring team may not always be in one central location.
Data Logging and Predictive Analytics:
Logging of Incidents and Events: The system logs all detected incidents, including the time, location, and camera feed details, which can be reviewed later for analysis or compliance reporting. This data logging helps in maintaining a record of fire safety incidents and system performance.
Predictive Analysis and Maintenance: The collected data is also used for predictive analytics. By analyzing historical data, the system can identify patterns, such as areas prone to frequent alerts or fire risks, enabling pre-emptive measures like additional surveillance or maintenance checks. This capability helps enhance overall fire safety and reduce future risks.
Security and Data Privacy:
Encryption of Video Feeds: To ensure security and privacy, the system encrypts all video feeds during transmission between CCTV cameras and the central processing unit. Encryption protocols like AES (Advanced Encryption Standard) are employed to prevent unauthorized access or tampering.
Data Access Control: The system includes access control measures, ensuring that only authorized personnel can view the footage or receive alerts. This prevents misuse of video data and ensures compliance with data protection regulations.
In conclusion, the proposed system effectively integrates CCTV technology with advanced image processing and machine learning to provide a comprehensive, scalable, and adaptable fire and smoke detection solution. By leveraging existing CCTV infrastructure, it reduces implementation costs and enhances fire safety through early detection, rapid response, and automatic activation of safety
The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present invention, and its practical application to thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present invention.
, Claims:1. The method as claimed in claim 2, wherein the scalability protocol includes steps for dynamically replicating data and balancing loads across the network to optimize storage efficiency.
A fire and smoke detection system, comprising:
one or more CCTV cameras strategically positioned to monitor areas of interest;
a central processing unit (CPU) connected to the CCTV cameras for receiving real-time video feed;
a software system deployed on the CPU, integrating image processing and machine learning algorithms for analyzing video frames to detect fire and smoke features;
an alert mechanism configured to trigger notifications and alarms when fire or smoke is detected;
an integration module for automatically communicating with emergency services and activating safety mechanisms.

2. A method for detecting fire and smoke, comprising the steps of:
a) capturing real-time video feeds using one or more CCTV cameras positioned to monitor designated areas;
b) transmitting the video feeds to a central processing unit (CPU) connected to the cameras;
c) analyzing the video frames using a software application that employs machine learning algorithms to detect visual features indicative of fire or smoke;
d) cross-referencing detected events across multiple cameras to verify the presence of fire or smoke and minimize false positives;
e) generating alerts and communicating with emergency response systems upon confirmation of a fire or smoke event.

3. The system as claimed in claim 1, wherein the CCTV cameras are connected to the CPU via a wireless communication network to enable flexible installation and remote monitoring.

4. The system as claimed in claim 1, wherein the software application uses convolutional neural networks (CNNs) for the detection of fire and smoke features within the video frames.

5. The system as claimed in claim 1, further comprising a mobile application that allows users to receive real-time alerts and monitor the video feeds remotely.

6. The system as claimed in claim 1, wherein the alert system sends notifications through multiple channels, including SMS, email, and integrated audio-visual alarms.

7. The system as claimed in claim 1, wherein the further incorporating data encryption protocols such as AES (Advanced Encryption Standard) to secure video feed transmissions between the CCTV cameras and the CPU.

8. The method as claimed in claim 2, wherein the software dynamically adjusts sensitivity settings for different monitored areas based on environmental factors such as lighting conditions and spatial characteristics.

9. The method as claimed in claim 2, wherein the system logs detected events and uses predictive analytics to identify high-risk zones for proactive fire safety management.

10. The method as claimed in claim 2, wherein the communication module automatically interfaces with emergency services and triggers connected fire suppression systems, such as sprinklers or gas extinguishers, upon detection confirmation.

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