Track & Count Objects using YOLOv8 ByteTrack & Supervision
Let’s build together an application to track and count objects using Computer Vision. We used YOLOv8 for detection, ByteTrack for tracking, and the latest python library from Roboflow – Supervision for object counting.
Chapters:
0:00 Introduction
1:28 Setting up the Python environment for vehicle tracking
5:28 Using YOLOv8 for vehicle detection
6:27 Building custom inference pipeline with Supervision for a single image
12:37 Building custom inference pipeline with Supervision for a whole video
15:46 Tracking detections with ByteTrack
17:40 Counting objects crossing the line with Supervision
19:29 Training YOLOv8 Object Detection model on custom dataset
22:50 Detect, track, and count candies on the conveyor
25:50 Conclusion
Resources:
🌏 Roboflow: https://roboflow.com
🌌 Roboflow Universe: https://universe.roboflow.com
⭐ Supervision repository: https://github.com/roboflow/supervision
📝 Track and Count with YOLOv8 Blogpost: https://blog.roboflow.com/yolov8-tracking-and-counting
📓 Track and Count Vehicles with YOLOv8 + ByteTRACK + Supervision Notebook: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-and-count-vehicles-with-yolov8.ipynb
📓How to Train YOLOv8 Object Detection on a Custom Dataset Notebook: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov8-object-detection-on-custom-dataset.ipynb
🎬 Count People in Zone | 3 Models: YOLOv5, YOLOv8 and Detectron2: https://youtu.be/l_kf9CfZ_8M
🎬 YOLOv8 Object Counting in Real-time with Webcam, OpenCV and Supervision: https://youtu.be/QV85eYOb7gk
🎬 YOLOv8: How to Train for Object Detection on a Custom Dataset: https://youtu.be/wuZtUMEiKWY
🎬 Instance Segmentation in 12 minutes with YOLOv8 and Python: https://youtu.be/pFiGSrRtaU4
📓 Learn more about YOLOv8 and other Computer Vision models with Roboflow Notebooks: https://github.com/roboflow/notebooks
Stay updated with the projects I’m working on at https://github.com/roboflow and https://github.com/SkalskiP! ⭐
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