AI-DRIVEN INTRUDER DETECTION WITH THERMAL IMAGING FOR REAL-TIME MONITORING

Abstract

A real-time intruder detection using thermal imaging and deep learning is disclosed. The system comprises a plurality of thermal cameras (001) positioned to capture thermal video and images 5 from specified regions. An object detection module (002) extracts thermal features to identify objects within the area, and a deep learning-based classification module (003) classifies the detected objects as human or non-human based on the extracted features. A movement tracking module (004) tracks object paths, identifying motion patterns indicative of suspicious behavior. An anomaly detection module (005) flags objects exhibiting deviations in movement or 10 temperature patterns as potential intruders. A digital twin simulation module (006) creates a virtual model of the area, predicting future movements and potential breach areas. An alert system (007) notifies security personnel in real time upon detecting intruders. A weather adjustment module (008) adapts the system's detection capabilities under varying environmental conditions to ensure consistent tracking and identification.

Specification

Complete Specification

" AI-Driven Intruder Detection with Thermal Imaging for Real-Time Monitoring"
FIELD OF INVENTION

[0001] The embodiments disclosed herein generally relates to artificial intelligence based intruder detection, and more particularly to a system and method for using Thermal Imaging and Deep Learning for the intruder detection.

BACKGROUND

[0002] Intruder detection plays a crucial role in ensuring the safety and security of a nation by protecting critical infrastructure, public spaces, and private properties from unauthorized access and potential threats. With growing concerns over terrorism, theft, and vandalism, effective surveillance systems have become essential for safeguarding sensitive areas, such as government buildings, military bases, borders, and industrial sites. Early detection of intruders helps prevent incidents that could lead to loss of life, economic damage, and breaches of national security. As threats evolve, advanced technologies like thermal imaging and deep learning are becoming integral to surveillance systems, providing realtime monitoring, accurate identification, and timely responses. These systems enhance the ability to track and neutralize threats before they escalate. Intruder detection is not only a matter of protecting assets but also a vital element in maintaining public safety, peace of mind, and the overall resilience of a nation's defense system.

[0003] Existing methods of land intruder detection are vital for ensuring national safety and security by monitoring and identifying unauthorized movements across borders and critical infrastructure. Techniques such as motion sensors, infrared cameras, and ground surveillance radar are commonly employed to detect intrusions in real-time. Additionally, drone technology and unmanned aerial vehicles (UAVs) enhance surveillance capabilities by providing aerial views of large areas and tracking suspicious activities. Integration of advanced analytics and artificial intelligence can further improve threat detection by analyzing patterns and identifying potential intruders swiftly. These methods collectively
contribute to a comprehensive security strategy, enabling timely responses to protect a nation's borders and sensitive locations.

[0004] The US patent application 18/153,108 "Machine learning based monitoring system"

5 discloses a system for using a machine learning to monitor individuals through camera

feeds. It first processes image data to detect if a person is present using a person detection model. Once a person is identified, the system analyzes subsequent images to determine if the person has fallen by applying a fall detection model. If a potential fall is detected, an alert is generated to notify relevant personnel.

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[0005] The US patent application 18/073,310 "Thermal imaging camera device" discloses a improved camera system features an uncooled thermal imaging sensor that can rotate 360 degrees thanks to a rotary actuator. The slip ring allows the sensor and its wiring to turn freely without any obstruction. An encoder tracks the sensor's angular position, ensuring

15 accurate aiming. This design enables effective monitoring while maintaining the

functionality of the thermal sensor.

[0006] The US patent application 18/525,899" Method and system for identifying reflections in thermal images" discloses method for detecting reflections by comparing a thermal image

20 captured by a thermal sensor with a second image taken by a visible light, near infrared, or

short-wave infrared sensor that overlaps the thermal image's field of view. It first identifies an object in the thermal image and then checks a corresponding area in the second image for an equivalent object. If no equivalent object is found in the second image, it concludes that the identified object in the thermal image is a reflection.

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[0007] Many existing patents do not disclose more advanced and optimized detection of intruders based on using advanced system and method comprising thermal imaging, deep learning and digital twin technologies.


OBJECTIVES
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[0008] The principal objective of this invention involves capture thermal video and images of areas within a specified region.

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[0009] The principal objective of this invention involves extract thermal features for identifying objects within the region and extracting thermal features for identifying objects within the region.

10 [0010] The principal objective of this invention involves classifying detected objects as human or

non-human based on extracted thermal features'and tracking the movement path of the detected objects for identifying patterns of motion indicative of intrusion or suspicious behavior.
15 [0011] The principal objective of this invention also involves detecting deviations in object

behavior, temperature patterns, or movement paths, flagging objects exhibiting unusual characteristics as potential intruders

[0012] In addition, the principal objective of this invention involves creating a digital twin model of the monitored area, simulating the paths and movements of detected objects within the real-world environment to predict future movement or potential breach areas.

[0013] In addition, the principal objective of this invention involves notify relevant authorities or security personnel in real time upon detection of an object classified as an intruder.

[0014] In addition, the principal objective of this invention involves analyzing and adapting the system's detection capabilities based on varying environmental conditions, ensuring consistent identification and tracking across different temperature and weather scenarios.



SUMMARY
[0015] The following presents a simplified summary of one or more examples in order to provide a basic understanding of the disclosure. This summary is not an extensive overview of all 5 contemplated examples, and is not intended to either identify key or critical elements of all

examples or delineate the scope of any or all examples. Its purpose is to present some concepts of one or more examples in a simplified form as a prelude to the more detailed description that is presented below.

10 [0016] The embodiments of the present invention describes a real-time intruder detection system

that utilizes thermal imaging and deep learning to identify, track, and respond to security breaches. The system includes thermal cameras to capture heat signatures within a monitored area, with an object detection module extracting features from the thermal data. A deep learning classification module distinguishes human from non-human objects, 15 followed by a movement tracking module that monitors object paths for suspicious

behavior. An anomaly detection module flags unusual patterns, and a digital twin simulation predicts potential breach routes by modeling the area in real time. If an intruder is detected, the alert system notifies authorities. Additionally, a weather adjustment module adapts detection based on environmental conditions, ensuring accuracy across various 20 climates and scenarios. This comprehensive system offers an advanced, adaptable solution

for enhancing security.

[0017] The embodiments of the present invention describes a method for real-time intruder detection using thermal imaging and deep learning to monitor and analyze movement in a 25 specified area. The method involves capturing thermal video and images via thermal

cameras to detect heat signatures of objects, followed by extracting thermal features such as shape, movement, and temperature variations. Using a deep learning model, the system classifies the detected objects as human or non-human, tracks their movement paths, and identifies intruder behavior based on anomalies like unusual movement patterns or 30 restricted area breaches. A digital twin simulation is generated to visualize and predict

object movement in real time, simulating potential intrusion routes. The detection process adapts to environmental factors like weather and time of day, ensuring accurate
classification under varying conditions. Upon detecting intruders or suspicious behavior, the system issues real-time alerts to security personnel for timely intervention.

[0018] To the accomplishment of the foregoing and related ends, the following description and annexed drawings set forth certain illustrative aspects and implementations. These are indicative of but a few of the various ways in which one or more aspects may be employed. Other aspects, advantages, and novel features of the disclosure will become apparent from the following detailed description when considered in conjunction with the annexed drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure -1 is an overview of a system for real-time intruder detection using thermal imaging and deep learning.

[0020] Figure - 2 is an overview of method for real-time intruder detection using thermal imaging and deep learning.

DETAILED DESCRIPTION j

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[0021] The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Those of ordinary skill in the art realize that the following descriptions of the embodiments of the present invention are illustrative and are not intended to be limiting in any way. Other embodiments of the present invention will readily suggest themselves to such skilled persons having the benefit of this disclosure. Like numbers refer to like elements throughout.

[0022] Although the following detailed description contains many specifics for the purposes of illustration, anyone of ordinary skill in the art will appreciate that many variations and alterations to the following details are within the scope of the invention. Accordingly, the following embodiments of the invention are set forth without any loss of generality to, and without imposing limitations upon, the claimed invention.

[0023] In this detailed description of the present invention, a person skilled in the art should note that directional terms, such as "above," "below," "upper," "lower," and other like terms are used for the convenience of the reader in reference to the drawings. Also, a person skilled in the art should notice this description may contain other terminology to convey position, orientation, and direction without departing from the principles of the present invention.

[0024] Furthermore, in this detailed description, a person skilled in the art should note that quantitative qualifying terms such as "generally," "substantially," "mostly," and other terms are used, in general, to mean that.the referred to object, characteristic, or quality constitutes a majority of the subject of the reference. The meaning of any of these terms is dependent upon the context within which it is used, and the meaning may be expressly modified.

[0025] Figure -1 illustrates an overview of a system for real-time intruder detection using thermal imaging and deep learning. The system includes a plurality of thermal cameras (001) positioned to capture thermal video and images of areas within a specified region; an object detection module (002) configured to receive the thermal video and images and extract thermal features for identifying objects within the region; a deep learning-based classification module (003) configured to classify detected objects as human or non-human based on extracted thermal features; a movement tracking module (004) configured to track the movement path of the detected objects, identifying patterns of motion indicative of intrusion or suspicious behavior; an anomaly detection module (005) configured to detect deviations in object behavior, temperature patterns, or movement paths, flagging objects exhibiting unusual characteristics as potential intruders; a digital twin simulation module (006) configured to create a virtual model of the monitored area, simulating the paths and movements of detected objects within the real-world environment to predict future movement or potential breach areas; an alert system (007) configured to notify relevant . authorities or security personnel in real time upon detection of an object classified as an intruder; and a weather adjustment module (008) configured to analyze and adapt the 5 system's detection capabilities based on varying environmental conditions, ensuring

consistent identification and tracking across different temperature and weather scenarios.

[0026] According to the embodiments of the present invention the plurality of thermal cameras (001) in the intruder detection system are strategically positioned to capture thermal video 10 and images of areas within a specified region. These cameras are typically placed at key

locations, ensuring full coverage of the monitored area, such as entrances, perimeters, or high-security zones. Their placement depends on factors like the layout of the site, potential intrusion points, and the need to avoid blind spots.

15 [0027] Plurality of thermal cameras work by detecting heat signatures rather than relying on visible

light, allowing them to function effectively in low light, nighttime conditions, or through obstacles like smoke or fog. To maximize their effectiveness, they are often installed at elevated points, such as poles, walls, or rooftops, to capture a wide field of view and minimize obstructions. In some cases, overlapping coverage is employed, where the 20 cameras are angled in such a way that their fields of view intersect, ensuring no part of the

area goes unmonitored.

[0028] Plurality of thermal cameras are continuously recording thermal data, which is then processed by the system to detect objects and classify them based on their heat profiles.

25 The use of multiple cameras enables a comprehensive monitoring system that can detect

even small or fast-moving intruders, regardless of environmental conditions, ensuring robust real-time surveillance.

[0029] According to the embodiments of the present invention the object detection module (002)

30 is integral to the intruder detection system, functioning by processing the thermal data

obtained from the thermal cameras (001). When the module receives thermal video and images, it employs a combination of computer vision and machine learning techniques to analyze the data. This involves extracting key thermal features such as temperature variations, shapes, and movement patterns that distinguish different objects within the monitored area.

[0030] For example, the module identifies heat signatures based on their intensity and spatial characteristics, which helps differentiate between humans and other objects, such as vehicles or animals. Techniques like background subtraction and contour detection may be used to isolate objects from the background, while feature extraction methods quantify characteristics like size, shape, and thermal intensity.

[0031] This information is crucial for accurately identifying and classifying objects, allowing the system to effectively monitor for potential intrusions or suspicious activities. Once the object detection module has processed and extracted these features, it sends the relevant data to the deep learning-based classification module for further evaluation and categorization.

[0032] According to the embodiments of the present invention the deep learning-based classification module (003) in the intruder detection system plays a pivotal role in categorizing detected objects as human or non-human using the thermal characteristics extracted by the object detection module (002). The deep learning-based classification module (003) leverages sophisticated neural network architectures, often convolutional neural networks (CNNs), which are particularly effective in processing visual data.

[0033] Once the deep learning-based classification module (003) receives the extracted thermal features, it processes them through multiple layers of the neural network. Each layer learns to identify increasingly complex patterns and characteristics associated with different object types. For instance, the network is trained on large datasets containing thermal images of various objects, allowing it to learn the unique heat signatures, shapes, and movement patterns that distinguish humans from other entities.

[0034] During classification, the deep learning-based classification module (003) assesses the input features against its learned representations to determine the likelihood of an object
being human or non-human. It produces output probabilities for each category, and based on a predefined threshold, it classifies the object accordingly. This classification process is highly adaptive; as the system encounters new scenarios or variations in thermal data, it can continuously improve its accuracy through further training and fine-tuning, ensuring reliable detection and enhanced security monitoring in real time.

[0035] According to the embodiments of the present invention the movement tracking module (004) is a critical component of the intruder detection system, designed to monitor and analyze the movement paths of detected objects over time.'Once the deep learning-based classification module (003) identifies an object, the movement tracking module (004) begins to follow its trajectory within the monitored area.

[0036] Using algorithms such as Kalman filters or optical flow techniques, the movement

tracking module (004) continuously estimates the position of objects based on their detected thermal signatures and movement patterns. It captures data on the object's speed, direction, and path, allowing it to build a comprehensive profile of its behavior. By storing this information, the module can recognize patterns indicative of intrusion or suspicious activity, such as lingering in restricted zones, making erratic movements, or approaching critical areas like entrances or perimeters. £

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[0037] Additionally, the movement tracking module (004) can analyze historical movement data to establish baseline behaviors for specific zones or times of day, helping to differentiate between normal and abnormal activities. When an object's movement deviates significantly from established patterns such as moving towards a high-security area or following an unusual route the system can flag this behavior as potentially suspicious. This capability not only enhances the effectiveness of real-time monitoring but also helps security personnel respond promptly to potential threats, increasing overall situational awareness.

[0038] According to the embodiments of the present invention the anomaly detection module (005) is an essential part of the intruder detection system, responsible for identifying and flagging objects that exhibit unusual behaviors or temperature patterns that may indicate potential
intrusions. The anomaly detection module (005) operates.by analyzing the movement and thermal characteristics of detected objects in real time, comparing them against established norms and expected behaviors within the monitored environment.

5 [0039] To achieve this, the anomaly detection module (005) first creates a baseline profile of

typical object behaviors, which includes expected movement patterns, temperature ranges, and dwell times in specific areas. This profiling is informed by historical data collected from previous surveillance activities. Once the baseline is established, the anomaly detection module (005) continuously monitors incoming data from the object detection and

10 movement tracking modules.

[0040] When an object's behavior significantly deviates from the established baseline-such as sudden increases in temperature, unexpected movement toward restricted areas, or abnormal dwell times in one location-the anomaly detection module flags it as suspicious.

15 The anomaly detection module (005) employs statistical analysis, machine learning

algorithms, and predefined thresholds to determine the significance of these deviations. For instance, if a detected object remains stationary in a high-security zone longer than usual or shows a heat signature characteristic of a human but behaves erratically, it raises a red flag.

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[0041] According to the embodiments of the present invention the digital twin simulation module (006) is a sophisticated component of the intruder detection system that creates a virtual representation of the monitored area, allowing for advanced analysis and prediction of object movements and potential breach areas. The digital twin simulation module (006)

25 leverages real-time data from the thermal cameras and the movement tracking module to

construct a dynamic and interactive model of the environment, including physical features, entry points, and typical patterns of human or object behavior.

[0042] To create this virtual model, the digital twin simulation module (006) integrates data such

30 . as the layout of the area, known pathways, and historical movement patterns. By simulating

the movements of detected objects within this model, the digital twin can predict future actions based on current trajectories and behaviors. For instance, if an object is moving
toward a particular location, the simulation can assess the likelihood of it continuing in that direction or taking alternative routes, considering various environmental factors like obstacles or changing weather conditions.

5 [0043] Additionally, the digital twin can be used to conduct "what-if" scenarios, allowing

security personnel to visualize potential intrusion paths and assess vulnerabilities in the monitored area. By analyzing various scenarios, the module can identify critical zones that may require heightened security measures or recommend adjustments to camera placements for improved coverage.

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[0044] Furthermore, the digital twin continuously updates based on new data inputs, ensuring that **§ the model remains accurate and relevant. By combining predictive analytics with real-time

data, the digital twin simulation module enhances the overall effectiveness of the intruder detection system, providing valuable insights for proactive security measures and informed

15 decision-making.

[0045] According to the embodiments of the present invention the alert system (007) is a crucial component of the intruder detection system, designed to ensure timely notifications to relevant authorities or security personnel when an object is classified as an intruder.

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[0046] When the deep learning-based classification module (003) identifies an object as a potential intruder, the alert system is activated. It quickly evaluates the classification's confidence level and cross-references it with any flagged anomalies detected by the anomaly detection module (005). If the object meets the predefined criteria for suspicious behavior, the alert

25 . system generates an immediate notification.

[0047] The notification process can involve multiple communication channels, including SMS, emails, mobile app alerts, or integration with existing security management systems. The alert typically includes critical information, such as the object's location, the nature of the

30 detected behavior, and any relevant video or thermal images. This ensures that security

personnel have all the necessary context to respond effectively.

[0048] According to the embodiments of the present invention the weather adjustment module (008) is an essential component of the intruder detection system that ensures consistent detection and tracking capabilities despite varying environmental conditions. The weather adjustment module (008) actively analyzes real-time weather data, such as temperature,

5 humidity, wind speed, and precipitation, which can significantly impact the performance

of thermal cameras and the overall effectiveness of the surveillance system.

[0049] By integrating data from weather sensors and forecasting services, the weather adjustment module (008) continuously monitors current conditions and anticipates changes that may

10 affect detection accuracy. For example, in colder temperatures, thermal signatures may be

less pronounced, making it more challenging to distinguish between objects. The weather adjustment module (008) compensates for this by fine-tuning the detection algorithms and thresholds used by the object detection and classification modules.

15 [0050] Additionally, the module can adjust parameters such as sensitivity levels and detection

ranges based on environmental conditions. For instance, during heavy rain or fog, which can obscure visibility, the module may enhance the system's ability to detect heat signatures by refining the algorithms to account for reduced visibility and altered temperature readings.'

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[0051] Furthermore, the weather adjustment module (008) can incorporate machine learning techniques to learn from historical data about how different weather conditions have affected detection performance in the past. This knowledge allows the system to proactively adjust its settings to maintain optimal detection capabilities under varying

25 conditions, ensuring reliable performance in diverse scenarios.

[0052] By effectively adapting to environmental factors, the weather adjustment module (008) enhances the overall robustness and accuracy of the intruder detection system, allowing for continuous and reliable monitoring regardless of changing weather conditions.

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[0053] Figure - 2 illustrates an overview of a method for real-time intruder detection using thermal imaging and deep learning, the method includes capturing, via a plurality of thermal
cameras (008), thermal video and images from a monitored area to detect heat signatures associated with objects in the area; extracting thermal features from the captured video and images (009), including shape, movement, and temperature variations of detected objects; classifying the detected objects, using a deep learning model (010), as human or non-5 human based on the extracted thermal features; tracking the movement paths of classified

objects (011) within the monitored area, using object segmentation and movement analysis techniques; identifying intruder behavior (012), by detecting anomalous movement patterns, restricted area breaches, or unusual temperature profiles; generating a digital twin simulation (013) of the monitored area, allowing visualization of object movements and 10 simulation of future movement paths based on current behavior; analyzing the spread of

detected objects (014) through the monitored area in real time to simulate potential intrusion routes and predict further movement; adapting the detection process based on environmental factors (015), including weather conditions and time of day, to maintain accurate classification and detection; and issuing a real-time alert to security personnel or 15 authorities (016) upon detection of an object classified as an intruder or exhibiting

suspicious movement patterns.





CLAIMS I / We Claim that,

1. A system for real-time intruder detection using thermal imaging and deep learning, comprising:

a plurality of thermal cameras (001) positioned to capture thermal video and images of areas 5 within a specified region;

an object detection module (002) configured to receive the thermal video and images and extract thermal features for identifying objects within the region;

a deep learning-based classification module (003) configured to classify detected objects as human or non-human based on extracted thermal features;

10 a movement tracking module (004) configured to track the movement path of the detected objects, identifying patterns of motion indicative of intrusion or suspicious behavior;

an anomaly detection module (005) configured to detect deviations in object behavior, temperature patterns, or movement paths, flagging objects exhibiting unusual characteristics as potential intruders;

15 a digital twin simulation module (006) configured to create a virtual model of the monitored area, simulating the paths and movements of detected objects within the real-world environment to predict future movement or potential breach areas;

an alert system (007) configured to notify relevant authorities or security personnel in real time upon detection of an object classified as an intruder; and

20 a weather adjustment module (008) configured to analyze and adapt the system's detection capabilities based on varying environmental conditions, ensuring consistent identification and tracking across different temperature and weather scenarios.

2. The system of claim 1, wherein the deep learning-based classification module (003) is further

25 configured to identify objects attempting to conceal or mask their heat signature using

thermal irregularity detection techniques.

The system of claim 1, wherein the movement tracking module (004) is further configured to store historical movement data and compare current object paths to past intrusion patterns for enhanced prediction and detection accuracy.

4. The system of claim 1, wherein the digital twin simulation module (006) is further configured

5 to allow real-time adjustments to camera positions, detection parameters, and alert thresholds

based on simulated intruder behavior.

5. The system of claim 1, wherein the alert system (007) is integrated with remote monitoring devices, enabling real-time response coordination across multiple locations and personnel.

6. A method for real-time intruder detection using thermal imaging and deep learning, the method 10 comprising:

capturing, via a plurality of thermal cameras (008), thermal video and images from a monitored area to detect heat signatures associated with objects in the area;

extracting thermal features from the captured video and images (009), including shape, movement, and temperature variations of detected objects;

15 classifying the detected objects, using a deep learning model (010), as humamor non-human

based on the extracted thermal features; ;i-:

tracking the movement paths of classified objects (011) within the monitored area, using object segmentation and movement analysis techniques;

identifying intruder behavior (012), by detecting anomalous movement patterns, restricted 20 area breaches, or unusual temperature profiles; '

generating a digital twin simulation (013) of the monitored area, allowing visualization of object movements and simulation of future movement paths based on current behavior;

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analyzing the spread of detected objects (014) through the monitored area in real time to simulate potential intrusion routes and predict further movement;


adapting the detection process based on'environmental factors (015), including weather conditions and time of day, to maintain accurate classification and detection; and

issuing a real-time alert to security personnel or authorities (016) upon detection of an object classified as an intruder or exhibiting suspicious movement patterns.

5 . 7. The method of claim 1, wherein the step of classifying detected objects further comprises detecting objects attempting to mask or conceal their heat signature using thermal anomaly detection algorithms.

8. The method of claim 1, wherein the step of tracking the movement paths (Oil) further includes storing historical movement data and comparing it with current paths to enhance 10 the prediction of intruder behavior.
9. The method of claim 1, wherein the digital twin simulation (013) is continuously updated based on real-time data from the monitored area, allowing security personnel to adjust detection thresholds and camera parameters dynamically.


10. The method of claim 1, wherein the system automatically adjusts detection sensitivity (015) based on changes in environmental conditions, such as temperature fluctuations or nighttime settings, to improve intruder detection.

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