Master Object Tracking & Counting with OpenCV & YOLO 🚀 | Hands-On Computer Vision Bootcamp

Check out the PRO version of the bootcamp: https://vizuara.ai/courses/hands-on-computer-vision-bootcamp-pro/

Language + Reasoning + Vision 3 in 1 bootcamp: https://3-in-1.vizuara.ai/

Welcome to the second live session of our Computer Vision Bootcamp hosted by Team Vizuara, where today we take a deep dive into building powerful computer vision applications using OpenCV and pre‑trained deep learning models. In our previous lecture, we introduced OpenCV in Python, explored the concept of filters, and even built a simple burglar detection algorithm using pixel difference logic and bounding rectangles. Today’s session is all about stepping things up 💡 — we will be using the YOLO v8 model (YOLO stands for You Only Look Once) to implement real time object detection 🎯, multi‑object tracking 🧠, object counting 📊, and object segmentation 🎨 to highlight actual object pixels rather than just drawing a box.

Throughout the session we discuss why detection, tracking, counting, and segmentation are all different tasks, and how OpenCV serves as the framework that makes visualization, annotation, and video processing intuitive, while YOLO handles the core intelligence behind the scenes. We walk through the Python code slowly and clearly — how to set up a local environment, install the ultralytics package, import YOLO v8, read images or capture live feeds using OpenCV, process continuous camera frames or videos, and evaluate pre‑trained models like YOLO v8 nano 🐣 for lightweight deployment.

We also cover how to ensure object IDs remain consistent across frames using the persist=True flag 🔁, how to count unique objects using sets and dictionaries, how to display bounding boxes, object IDs, centroids, and even trails 🧵 showing motion over time. You will also learn how to save these annotated outputs as video files using OpenCV.

Then we move on to segmentation — here, YOLO's pixel‑level masks are extracted, resized, and used to highlight object shapes with accurate contours 🧩. We add visual overlays to see how well segmentation performs in real time and compare this to the simpler bounding box detection. The moment you see the difference, you will truly understand what segmentation brings to the table.

We also discuss practical challenges like dealing with transparent bottles 🍼, fast‑moving scenes like conveyor belts ⚙️, overlapping objects in aerial videos 🛰️, and how to tune these pipelines for real‑world deployments. Whether you want to detect people on the street, count vehicles in traffic 🚗, or inspect industrial pipelines for cracks 🔍 — this lecture equips you with a foundation to start building real applications.

Towards the end, we give a small peek into what’s coming next — R‑CNN, Fast R‑CNN, Faster R‑CNN, and Mask R‑CNN — where we will explore even more powerful and refined approaches for detection and segmentation.

What you will learn

How to build real‑time object detection and segmentation pipelines using YOLO v8

How to track and count objects using unique IDs across video frames

How to draw centroid trails and save annotated videos

How to use YOLO masks for pixel‑level segmentation and compare it with detection

Real‑world applications in surveillance, manufacturing, traffic analysis, and drone videos

So keep your system ready, make sure your webcam or video input is working, install ultralytics and OpenCV, and follow along step by step. Try it out on your own videos, and share your experiments and observations in our Discord community. See you soon in the next session — we are just getting started!