Object Detection Projects | Computer Vision | YOLOv8 | OpenCV | Video - 2 #shorts #traffic #yolov8

This video shows an end‑to‑end pipeline that uses YOLOv8 and BoT‑SORT to detect and track objects in videos, and adds a terminal-style console overlay so viewers can see model, FPS, frame count, and tracked object count in real time.

What you’ll learn:

Git Repo : https://github.com/ayushpawar21/YOLOVisionTrack360Pro.git

How to run the project locally and pick a YOLOv8 model
How tracking IDs are assigned and how overlays are rendered
How to export a clean, MP4 with console panels

Quick start (copy to terminal):

Place input clips in the videos folder
Choose model when prompted (1-5 or D for default)
Choose videos (A = all or list indices)

Files to check:
main.py — entry point and interactive menus
utils.py — frame annotation and console overlay functions
tracker.py — YOLOv8 + BoT‑SORT tracker configuration

Tips:
For fast tests use yolov8n.pt. For higher accuracy choose yolov8x.pt (larger download).
If GPU is available, YOLO will auto-detect and use it for faster processing.
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Видео Object Detection Projects | Computer Vision | YOLOv8 | OpenCV | Video - 2 #shorts #traffic #yolov8 канала TechwithAayushPawar