Color-Based Object Detection & Tracking

A real-time color-based object detection and tracking system built with Python and OpenCV. Supports 5 switchable detection modes, multi-object tracking, motion trail visualization, and a live HSV tuning interface.

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Features

• 5 Detection Modes — Green, Red, Blue, Multi (all 3 simultaneously), and Custom HSV slider mode
• Dual-range red masking — handles red's hue wrapping at 0°/180° in HSV color space
• 30-point motion trail — visualizes object movement history per color channel
• 3-step mask cleaning pipeline — median blur → morphological open → morphological close for noise-robust detection
• Live HSV tuner — interactive trackbar controls to dial in custom color ranges in real time
• Contour-based tracking — bounding boxes and center-point labels rendered on the largest detected contour per color

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Demo

Mode	Description
S	Custom HSV slider mode — tune your own color range live
G	Green object tracking
R	Red object tracking (dual-range HSV)
B	Blue object tracking
M	Multi-color mode — tracks Green, Red, and Blue simultaneously

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How It Works

1. Color Space Conversion

Each frame is converted from BGR to HSV, which separates color (hue) from lighting (value) — making detection far more robust under changing light conditions than RGB.

2. Masking

• Green and Blue use a single HSV range via cv2.inRange()
• Red uses two ranges combined with bitwise OR, since red straddles the 0°/180° boundary in the HSV hue wheel

3. Mask Cleaning

Each mask goes through a 3-step pipeline:

Median Blur (5x5)        → removes salt-and-pepper noise
Morphological Open       → eliminates small false-positive blobs
Morphological Close      → fills small holes inside detected regions

4. Tracking

• Contours are extracted with cv2.findContours()
• The largest contour is selected as the tracked object
• A bounding box and color label are drawn around it
• The center point is appended to a rolling 30-point trail, drawn as connected line segments

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Project Structure

Color-Based-Object-Tracking/
├── main.py               # Main application loop

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Installation

git clone https://github.com/VidhanGupta-01/Color-Based-Object-Tracking.git
cd Color-Based-Object-Tracking
pip install opencv-python numpy

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Usage

python main.py

Once running, use keyboard shortcuts to switch modes:

Key	Action
S	Switch to HSV slider (custom) mode
G	Switch to green tracking mode
R	Switch to red tracking mode
B	Switch to blue tracking mode
M	Switch to multi-color tracking mode
Q	Quit

Three windows will open:

• Original — live feed with tracking overlay
• HSV — raw HSV frame
• Mask — current active detection mask

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Requirements

opencv-python
numpy

A working webcam is required.

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Tech Stack

• Python 3
• OpenCV — camera capture, HSV conversion, contour detection, drawing
• NumPy — array operations for HSV range definitions

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Author

Developed by Vidhan Gupta
GitHub · LinkedIn