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! ⭐