Lilly Tech Systems
Build a Computer Vision App
Build a production-ready object detection and tracking application from scratch. This hands-on project walks you through YOLOv8 inference, video processing, multi-object tracking, analytics dashboards, and deploying a Streamlit web interface — all with full working code.
Project Build Path
Follow these lessons in order to build the complete project step by step, or jump to any section you need.
1. Project Setup
Architecture overview, YOLOv8 and OpenCV basics, tech stack, and project scaffolding.
2. Object Detection
YOLO inference, bounding boxes, class filtering, and confidence thresholds.
3. Video Processing
Frame extraction, real-time webcam, video file input, and processing pipelines.
4. Object Tracking
ByteTrack/SORT algorithms, persistent track IDs, and trail visualization.
5. Analytics
Counting, dwell time, heatmaps, and zone detection.
6. Web Interface
Streamlit dashboard for upload, webcam, and data export.
7. Enhancements
Edge deployment, custom training, alerts, and FAQ.
What You Will Build
By the end of this project, you will have a fully functional application that can:
Detect Objects in Real-Time
Use YOLOv8 to detect and classify objects in images, video files, and live webcam feeds.
Track Objects Across Frames
Assign persistent IDs and track movement across video frames using ByteTrack.
Generate Analytics
Count objects, measure dwell times, create heatmaps, and detect zone events.
Serve via Web Interface
Deploy a Streamlit web app for video upload, live webcam, and results export.