Referenced paper : Weighted Unsupervised Learning for 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.

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.

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.

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.

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.

@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},
}