Depth-segmentation

Real-time Background Segmentation with Depth Camera

Prerequisites and Requirements:

Before we begin, make sure you have the following prerequisites:

• Intel® RealSense™ SDK installed.
• Depth camera (D405 or D435i).
• Basic knowledge of Python.

For the project, you'll need Python 3 packages: pyrealsense2 and OpenCV (for displaying and processing output).

Image Segmentation: Image segmentation is essential in computer vision tasks, and there are various techniques like Thresholding, Edge detection, Clustering, and Region-based approaches. However, for this project, we'll explore depth camera-based segmentation, which has some exciting advantages:

1. Real-time operation.
2. Low computational requirements (can be run on a CPU effortlessly).
3. No Machine Learning Training Required: We don't need pre-trained models, saving time and effort.

A Sample Use Case Application with Background Segmentation and Object Detection:

To showcase the practicality of background segmentation, I present a fascinating use case: combining it with object detection for high-throughput phenotyping in soybean yield estimation. By removing irrelevant elements using background segmentation, we can focus solely on the soybean pods of interest. I compare the performance of the object detection model with and without background removal, showing significant improvements when the background is segmented.

Conclusion:

In conclusion, real-time background segmentation is essential for various computer vision applications. Using depth cameras like the Intel® RealSense™ D405 in combination with Python3 offers a simple and lightweight solution without complex machine learning algorithms. I hope you find my article useful, and I'm excited to see the fun applications you come up with.