Real-Time Object and Movement Detection | OpenCV | YOLOv3
In this video, we’ll walk you through implementing real-time object detection and movement tracking using YOLOv3 and OpenCV in Python. Learn how to set up the YOLOv3 model, process video frames from your webcam, and detect both objects and their movement across frames.
What You’ll Learn:
• How to load the pre-trained YOLOv3 model and COCO class labels.
• Preparing video frames for object detection using OpenCV’s DNN module.
• Implementing Non-Maximum Suppression (NMS) to refine detection results.
• Calculating movement by comparing object positions between frames.
• Annotating video frames with bounding boxes and movement status.
Code Overview:
We’ll start by loading the YOLOv3 model and the COCO dataset class labels. Using OpenCV, we’ll capture video frames from your webcam and preprocess them for object detection. The YOLOv3 model will predict bounding boxes for detected objects, which we’ll refine using Non-Maximum Suppression.
To detect movement, we’ll compare the positions of detected objects across consecutive frames. If an object moves beyond a defined threshold, we’ll label it as “Moving”; otherwise, it’ll be labeled as “Not Moving.”
Prerequisites:
• Basic understanding of Python programming.
• Familiarity with OpenCV and NumPy libraries.
• OpenCV and NumPy installed in your Python environment.
Get the code and all the files here
https://learnapplybuild.graphy.com/pr...