Revolutionizing Surveillance: Cameras with AI-Powered Object Detection

In today’s fast-paced world, simply having surveillance cameras isn’t enough. What if your cameras could not only record but also understand and interpret what they see?

This isn’t a vision of the future—it’s happening now. By leveraging artificial intelligence (AI) and machine learning, your Real-Time Streaming Protocol-capable (RTSP-capable) cameras can be transformed into a powerful, intelligent surveillance system.

The Power and Simplicity of AI Computer Vision

AI computer vision of RTSP-capable cameras are unlocked when the video stream is fed into a device (e.g. Raspberry-Pi) running Python, OpenCV with pre-trained COCO dataset. If your existing IP cameras is RTSP-capable, the takeaway is that you can follow the below instructions to add AI computer vision for object detection to improve operational efficiency and security with some 11 lines of Python code with OpenCV library.

The COCO dataset

COCO stands for Common Objects in Context, is a large-scale object detection, segmentation, and captioning dataset. It is widely used in computer vision research and development. Here are some key features of the COCO dataset:

Diverse Objects: COCO contains images of complex everyday scenes with various objects, providing a wide range of context.
Annotations: The dataset includes extensive annotations for several tasks:
- Object Detection: Bounding boxes and labels for over 80 object categories.
- Segmentation: Both instance segmentation (detailed pixel-level outlines for individual object instances) and semantic segmentation (pixel-level classification for each object category).
- Keypoint Detection: Annotations for human keypoints (e.g., joints like elbows, knees) for pose estimation.
- Image Captioning: Descriptive captions for images to support tasks like image description generation.

Size: COCO consists of over 200,000 labelled images with more than 2.5 million labelled instances, making it one of the most comprehensive datasets available for computer vision tasks.

Architecture Diagram

Figure 1: Security camera to AI computer vision processing

RTSP Streaming: Most IP cameras can stream live video using RTSP
Processing: The RTSP feed is routed to an external system running OpenCV, equipped with pre-trained models from the COCO dataset (e.g. Raspberry-PI)
Object Detection: This setup allows for real-time identification and classification of 80 commonly recognized objects using the YOLO (You Only Look Once) object detection model.
Actionable Insights: Detected objects can trigger alerts, automate processes, and provide valuable data for analysis.

Code Snippets

Below 11 lines of python code (e.g. run in Raspberry-pi) shows how simple it is to use IP camera’s RTSP stream to detect objects using COCO data set.

import cv2
import cvlib as cv
from cvlib.object_detection import draw_bbox


#live camera feed from IP camera via Real time streaming protocol (RTSP)
video = cv2.VideoCapture("rtsp://[your rtsp ip address]/live")
while True:
    ret, frame = video.read()
    bbox, label, conf = cv.detect_common_objects(frame)
    output_image = draw_bbox(frame, bbox, label, conf)
    cv2.imshow("Object Detection", output_image)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

Real-World Applications

The integration of AI and computer vision with RTSP-capable cameras opens up numerous possibilities:

Enhanced Security: Automatically detect unauthorized access, suspicious activities, or specific objects, and instantly alert security personnel.
Retail Optimization: Monitor customer behavior, manage inventory, and optimize store layouts by analyzing interactions with products.
Traffic Management: Improve urban planning and traffic control by analyzing vehicle and pedestrian flows.
Industrial Automation: Oversee manufacturing processes, monitor safety compliance, and improve operational efficiency by detecting specific objects and anomalies.

Supercharge your IP cameras now with AI

By integrating RTSP-capable cameras with OpenCV and YOLO, your IP cameras which are RTSP-capable can instantly become smarter with AI object detection capabilities, enhancing security and operational efficiency.

About the Author

Felix is the APAC Head of Sales Engineering at Cisco Meraki, leading a team of world-class and passionate sales engineers to help customers improve business outcomes with SASE, SD-WAN, security, Wi-Fi6, API-driven solutions, behavioural analytics, video surveillance and mobile device management (MDM) solutions.

He is passionate about technology, driving growth culture while building well-rounded teams.

References

https://youtu.be/V62M9d8QkYM (Great tutorial)
https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov7.cfg (download yolo config and weights)
Source Code: https://github.com/Kent-Taylor/object-detection/blob/main/main.py

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