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https://github.com/yukebrillianth/opencv-object-tracking-with-distance-measurement
Object Tracking By Color With Distance Measurement By Polynomial Regression
https://github.com/yukebrillianth/opencv-object-tracking-with-distance-measurement
computer-vision cpp distance-measures opencv
Last synced: 21 days ago
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Object Tracking By Color With Distance Measurement By Polynomial Regression
- Host: GitHub
- URL: https://github.com/yukebrillianth/opencv-object-tracking-with-distance-measurement
- Owner: yukebrillianth
- Created: 2024-10-20T05:10:05.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-10-24T15:19:29.000Z (26 days ago)
- Last Synced: 2024-10-26T05:32:27.645Z (24 days ago)
- Topics: computer-vision, cpp, distance-measures, opencv
- Language: C++
- Homepage:
- Size: 38.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Object Tracking By Color With Distance Measurement By Polynomial Regression
> Name: Yuke Brilliant Hestiavin
> Departement: Computer Engineering
> Nrp: 5024241016
This project is related to the task as an intern software engineer in the Robotics UKM Research Team (IRIS) week 2, Computer Vision chapter.
- [Object Tracking By Color With Distance Measurement By Polynomial Regression](#object-tracking-by-color-with-distance-measurement-by-polynomial-regression)
- [Demo](#demo)
- [TO-DO: Implementing Polynomial Regression for Distance Calculation](#to-do-implementing-polynomial-regression-for-distance-calculation)
- [Step 1: Data Collection](#step-1-data-collection)
- [Step 2: Regression Analysis](#step-2-regression-analysis)
- [Step 3: Code Implementation](#step-3-code-implementation)
- [Step 4: Testing and Calibration](#step-4-testing-and-calibration)
- [Current Progress](#current-progress)
- [Next Steps](#next-steps)
- [Dependencies](#dependencies)
- [How to Use](#how-to-use)
- [1. Installation](#1-installation)
- [2. Usage](#2-usage)
- [Calibration Data](#calibration-data)
## Demo
https://github.com/user-attachments/assets/e4524a0b-ffca-4f21-9e84-1798322f5573
## TO-DO: Implementing Polynomial Regression for Distance Calculation
### Step 1: Data Collection
- [x] Set up the camera in a fixed position
- [x] Prepare an object with known dimensions (preferably the same color as your tracking target)
- [x] Mark several distances on the floor (e.g., every 10cm from 10cm to 100cm)
- [x] Collect radius measurements at each distance point
- [x] Record at least 4 data pairs of radius (pixels) and actual distance (cm)
### Step 2: Regression Analysis
- [x] Use regression calculator (arachnoid.com)
- [x] Input the collected data pairs
- [x] Set polynomial degree
### Step 3: Code Implementation
- [x] Create calculateDistance() function using polynomial coefficients
- [x] Implement quadratic equation solver
- [ ] Add error handling for invalid radius values
- [ ] Add boundary checking for calculated distances
- [x] Integrate the function with the main tracking loop
### Step 4: Testing and Calibration
- [x] Test the system at known distances
- [ ] Calculate error margins
- [ ] Adjust coefficients if necessary
- [x] Document the accuracy at different distances
## Current Progress
- [x] Basic color tracking implemented
- [x] Contour detection working
- [x] Minimum enclosing circle calculation added
## Next Steps
- [x] Complete the polynomial regression implementation
- [x] Add detailed documentation
- [ ] Optimize for better accuracy
## Dependencies
- OpenCV 4.x
- C++ 11 or higher
## How to Use
### 1. Installation
Clone this repo
```bash
git clone https://github.com/yukebrillianth/opencv-object-tracking-with-distance-measurement.git distance-measuring
cd distance-measuring
```
Make a build directory
```bash
mkdir build
cd build
```
run cmake
```bash
cmake ..
```
### 2. Usage
compile before run the program
*(run in build directory)*
```bash
make
```
run the program
```bash
./DistanceMeasurement
```
## Calibration Data
```
Radius (pixels) | Distance (cm)
----------------|---------------
254 | 10
142 | 20
105 | 30
78 | 40
65 | 50
46 | 70
Mode: normal x,y analysis
Polynomial degree 3, 8 x,y data pairs.
Correlation coefficient = 0.9943957141940097
Standard error = 2.64455698786371
Output form: simple list (ordered x^0 to x^n):
1.7015679224939916e+002
-2.8134763868498247e+000
1.7367481282684945e-002
-3.4546683821398511e-005
```