https://github.com/kk7nc/3d-object-detection

Weighted Unsupervised Learning for 3D Object Detection
https://github.com/kk7nc/3d-object-detection

clustering kinect machine-learning object-detection unsupervised-machine-learning

Last synced: about 2 months ago
JSON representation

Weighted Unsupervised Learning for 3D Object Detection

• Host: GitHub
• URL: https://github.com/kk7nc/3d-object-detection
• Owner: kk7nc
• License: gpl-3.0
• Created: 2018-07-26T00:58:59.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-08-01T01:09:54.000Z (almost 7 years ago)
• Last Synced: 2025-05-06T23:16:32.362Z (about 2 months ago)
• Topics: clustering, kinect, machine-learning, object-detection, unsupervised-machine-learning
• Language: C++
• Homepage:
• Size: 7.67 MB
• Stars: 13
• Watchers: 4
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Weighted Unsupervised Learning for 3D Object Detection

[![DOI](https://img.shields.io/badge/DOI-10.14569/IJACSA.2016.070180-blue.svg?style=flat)](http://dx.doi.org/10.14569/IJACSA.2016.070180)

[![DOI](https://img.shields.io/badge/Visual%20Studio-C%2B%2B-red.svg)](https://visualstudio.microsoft.com/)

[![L](https://img.shields.io/aur/license/yaourt.svg)](https://github.com/kk7nc/3D-Object-Detection/blob/master/LICENSE)

[![twitter](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Weighted%20Unsupervised%20Learning%20for%203D%20Object%20Detection%0aGitHub:&url=https://github.com/kk7nc/3D-Object-Detection&hashtags=3D,ObjectDetection,unsupervised,MachineLearning,Clustering,RGBD,Computer_Vision,Kinect)

Referenced paper : [Weighted Unsupervised Learning for 3D Object Detection](https://arxiv.org/pdf/1602.05920.pdf)

3D Object Detection:

=====================

This paper introduces a novel weighted unsupervised

learning for object detection using an RGB-D camera. This

technique is feasible for detecting the moving objects in the noisy

environments that are captured by an RGB-D camera. The main

contribution of this paper is a real-time algorithm for detecting

each object using weighted clustering as a separate cluster. In a

preprocessing step, the algorithm calculates the pose 3D position

X, Y, Z and RGB color of each data point and then it calculates

each data point’s normal vector using the point’s neighbor. After

preprocessing, our algorithm calculates k-weights for each data

point; each weight indicates membership. Resulting in clustered

objects of the scene.

![Object_Detection](http://kowsari.net/onewebstatic/Overview_Object.png)

Pipeline of 3D Object detection using RGB-D camera has two main parts: 1) Preprocessing including Mapping, Back-Projection,  Normal  Generating,  Background  removal  and  2)  Clustering  including  assigned  initial  weight,  distance  calculation,update weight and assign color, and finally visualization to illustrate the results.

Results:

=====================

![Object_Detection](http://kowsari.net/onewebstatic/OBJECT%20(1).jpg)

Kinect color frame (RGB) with resolution of 1920 X 1080; b) Kinect depth frame with resolution of 512 X 424; c) Proposed method object detection using k= 15 clusters, and after 15 iterations.

![Object_Detection](http://kowsari.net/onewebstatic/OBJECT%20(3).jpg)

a) Kinect color frame (RGB) with resolution of 1920 X 1080; b) Kinect depth frame with resolution of 512 X 424; c)  Proposed  method  object  detection  using  k=  7  clusters,  and  after  10  iterations.  Memory  consumption  is  320  MB  and  framerate is 8.1±0.2FPS.

![Object_Detection](http://kowsari.net/onewebstatic/OBJECT%20(2).jpg)

Results  of  segmenting  scene  objects  using  proposed  algorithm;a)  Segmentation  of  small  duck;b)  Segmentation  anddetection  of  piece  of  red  paper;c)  Object  detection  of  a  box;d)  Shows  handy  bag;e)  Segmentation  of  box,  the  border  of  thebox  has  lower  weight  and  it  will  be  completed  after  several  iteration;f)  Representation  of  moving  object,  segmentation  of  aperson;g) Segmentation of basketball.

Citations

---------

```

@article{Kowsari2016,

title = {Weighted Unsupervised Learning for 3D Object Detection},

journal = {International Journal of Advanced Computer Science and Applications}

doi = {10.14569/IJACSA.2016.070180},

url = {http://dx.doi.org/10.14569/IJACSA.2016.070180},

year = {2016},

publisher = {The Science and Information Organization},

volume = {7},

number = {1},

author = {Kamran Kowsari and Manal H. Alassaf},

}

```