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wastetracker

$200 Original price was: $200.$150Current price is: $150.

. Developed an AI-powered waste sorting and counting system using Python, OpenCV, Tkinter, and YOLO.
. Supported webcam, IP camera, and video file inputs for real-time monitoring.
. Detected and tracked multiple waste objects using YOLO and ByteTrack.
. Implemented a user-defined sorting line for conveyor belt monitoring.
. Counted waste items per waste category after crossing the sorting line.
. Displayed bounding boxes, tracking IDs, and class labels in real time.
. Included video playback controls such as pause, restart, seek, and loop.
. Showed live waste statistics for each detected class.
. Allowed users to reset counts and redraw the sorting line without restarting.
. Provided a responsive GUI with multithreaded real-time processing.

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Category: Projects Tags: AI Projects, Artificial Intelligence, Computer Vision, Detection, machine learning, Object detection, Real-time monitoring, Smart Surveillance, Supervision, video analytics
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Description

♻️ AI Waste Sorting & Conveyor Monitoring System

A real-time computer vision application built with Python, YOLO, ByteTrack, and Tkinter — detecting, tracking, and counting waste items on a conveyor belt by category.

Built on OpenCV, Tkinter, NumPy, PIL, and Ultralytics YOLO, the system automatically pulls waste categories straight from the trained model — no code changes needed to support new classes. Each item is tracked with a persistent ID as it moves along the belt and counted per category as it crosses a user-drawn sorting line, giving accurate, duplicate-free statistics in real time.

⚙️ How It Works

🖥️

Interactive Desktop Interface

Connect to a webcam, IP camera, or recorded conveyor footage, with full playback controls — pause, restart, seek, and loop.

🧠

Persistent Waste Tracking

ByteTrack assigns each waste item a unique ID, preserving its identity across frames to eliminate duplicate counting.

📏

Custom Sorting Line

A simple click-and-drag lets users define the counting region directly on the conveyor image, auto-scaled for accuracy.

🗂️

Per-Category Counting

Each crossing event increments the counter for its specific waste class — plastic, paper, metal, glass, or any trained category.

🧹

Smart Track Management

Inactive tracking IDs are periodically cleared, keeping memory usage low and counts accurate during long-running operation.

🔁

Multithreaded Performance

Frame capture and detection run on a background thread, keeping the interface smooth and responsive at all times.

📊 Live Waste Statistics HUD

Items Currently on Belt
Per-Category Counts
Tracking IDs
Sorting Line Position
Playback Status
Counter Reset Controls

🎥 Supported Input Sources

Live Webcam
IP Camera
Recorded Conveyor Belt Video
Pause / Restart / Seek / Loop

🏷️ Detected Waste Categories

Plastic
Paper
Metal
Glass
+ Any Trained Category

🌍 Real-World Applications

🏭 Industrial Automation

♻️ Recycling Facility Sorting

🌱 Smart Waste Management

📈 Waste Volume Analytics

✅ Project Highlights

✔ Real-time per-category waste counting
✔ Persistent tracking prevents duplicate counts
✔ User-drawn, scale-accurate sorting line
✔ Auto-loaded waste classes from trained model
✔ Live HUD with per-class statistics
✔ Full playback control for recorded footage
✔ Automatic inactive-track cleanup
✔ Smooth, freeze-free multithreaded UI

🚀 Conclusion

The AI Waste Sorting & Conveyor Monitoring System combines deep learning detection, multi-object tracking, interactive line-crossing analysis, and per-class counting into one complete, industrial-ready solution — built for recycling facilities, automated sorting lines, and smart waste management systems.

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