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Security Alarm System Project Using Ultralytics YOLO11

Security Alarm System

The Security Alarm System Project utilizing Ultralytics YOLO11 integrates advanced computer vision capabilities to enhance security measures. YOLO11, developed by Ultralytics, provides real-time object detection, allowing the system to identify and respond to potential security threats promptly. This project offers several advantages:

  • Real-time Detection: YOLO11's efficiency enables the Security Alarm System to detect and respond to security incidents in real-time, minimizing response time.
  • Accuracy: YOLO11 is known for its accuracy in object detection, reducing false positives and enhancing the reliability of the security alarm system.
  • Integration Capabilities: The project can be seamlessly integrated with existing security infrastructure, providing an upgraded layer of intelligent surveillance.



Watch: Security Alarm System Project with Ultralytics YOLO11 Object Detection

Code

Note

App Password Generation is necessary

  • Navigate to App Password Generator, designate an app name such as "security project," and obtain a 16-digit password. Copy this password and paste it into the designated password field in the code below.

Security Alarm System using YOLO11 Example

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("Path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"

# Video writer
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
video_writer = cv2.VideoWriter("security_alarm_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

from_email = "abc@gmail.com"  # The sender email address
password = "---- ---- ---- ----"  # 16-digits password generated via: https://myaccount.google.com/apppasswords
to_email = "xyz@gmail.com"  # The receiver email address

# Init SecurityAlarm
security = solutions.SecurityAlarm(
    show=True,  # Display the output
    model="yolo11n.pt",  # i.e. YOLO11s.pt
    records=1,  # Total detections count to send an email about security
)

security.authenticate(from_email, password, to_email)  # Authenticate the email server

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = security.monitor(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

That's it! When you execute the code, you'll receive a single notification on your email if any object is detected. The notification is sent immediately, not repeatedly. However, feel free to customize the code to suit your project requirements.

Email Received Sample

Email Received Sample

Arguments SecurityAlarm

Here's a table with the SecurityAlarm arguments:

Name Type Default Description
model str None Path to Ultralytics YOLO Model File
line_width int 2 Line thickness for bounding boxes.
show bool False Flag to control whether to display the video stream.
records int 5 Total detections count to send an email about security.

Arguments model.track

Argument Type Default Description
source str None Specifies the source directory for images or videos. Supports file paths and URLs.
persist bool False Enables persistent tracking of objects between frames, maintaining IDs across video sequences.
tracker str botsort.yaml Specifies the tracking algorithm to use, e.g., bytetrack.yaml or botsort.yaml.
conf float 0.3 Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives.
iou float 0.5 Sets the Intersection over Union (IoU) threshold for filtering overlapping detections.
classes list None Filters results by class index. For example, classes=[0, 2, 3] only tracks the specified classes.
verbose bool True Controls the display of tracking results, providing a visual output of tracked objects.

FAQ

How does Ultralytics YOLO11 improve the accuracy of a security alarm system?

Ultralytics YOLO11 enhances security alarm systems by delivering high-accuracy, real-time object detection. Its advanced algorithms significantly reduce false positives, ensuring that the system only responds to genuine threats. This increased reliability can be seamlessly integrated with existing security infrastructure, upgrading the overall surveillance quality.

Can I integrate Ultralytics YOLO11 with my existing security infrastructure?

Yes, Ultralytics YOLO11 can be seamlessly integrated with your existing security infrastructure. The system supports various modes and provides flexibility for customization, allowing you to enhance your existing setup with advanced object detection capabilities. For detailed instructions on integrating YOLO11 in your projects, visit the integration section.

What are the storage requirements for running Ultralytics YOLO11?

Running Ultralytics YOLO11 on a standard setup typically requires around 5GB of free disk space. This includes space for storing the YOLO11 model and any additional dependencies. For cloud-based solutions, Ultralytics HUB offers efficient project management and dataset handling, which can optimize storage needs. Learn more about the Pro Plan for enhanced features including extended storage.

What makes Ultralytics YOLO11 different from other object detection models like Faster R-CNN or SSD?

Ultralytics YOLO11 provides an edge over models like Faster R-CNN or SSD with its real-time detection capabilities and higher accuracy. Its unique architecture allows it to process images much faster without compromising on precision, making it ideal for time-sensitive applications like security alarm systems. For a comprehensive comparison of object detection models, you can explore our guide.

How can I reduce the frequency of false positives in my security system using Ultralytics YOLO11?

To reduce false positives, ensure your Ultralytics YOLO11 model is adequately trained with a diverse and well-annotated dataset. Fine-tuning hyperparameters and regularly updating the model with new data can significantly improve detection accuracy. Detailed hyperparameter tuning techniques can be found in our hyperparameter tuning guide.

📅 Created 1 year ago ✏️ Updated 27 days ago

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