loading
Documents

Research.Publish.Connect.

Paper

Authors: Adil Sarsenov ; Aigerim Yessenbayeva ; Almas Shintemirov and Adnan Yazici

Affiliation: Department of Computer Science, Nazarbayev University, 53 Kabanbay Batyr Ave, Nur-Sultan, Kazakhstan

ISBN: 978-989-758-479-4

Keyword(s): Deep Learning, Object Detection, Trajectory, Depth Camera, LIDAR.

Abstract: Nowadays, there are many different approaches to detect objects as well as to determine the trajectory of an object. Each of these approaches has its advantages and disadvantages in terms of real-time use for various applications. In this study, we propose an approach to detect objects in real-time using the YOLOv3 deep learning algorithm and plot the trajectory of an object using 2D LIDAR and depth cameras on a robot. The laser rangefinder allows us to find distances to objects from a certain angle, but does not provide accurate object detection of the object class. In order to detect the object in real-time and discover the class to which the object belongs, we formed YOLOv3 deep learning model using transfer learning on several classes from data sets of publicly accessible images. We also measured the distance to an object using a depth camera with LIDAR together to determine and estimate the trajectory of objects. In addition, these detected trajectories are smoothed by polynomial regression. Our experiments in a laboratory environment show that YOLOv3 with 2D LIDAR and depth camera on a controlled robot can be used fairly accurately and efficiently in real-time situations for the detection of objects and trajectories necessary for various applications. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.236.156.34

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Sarsenov, A.; Yessenbayeva, A.; Shintemirov, A. and Yazici, A. (2020). Detection of Objects and Trajectories in Real-time using Deep Learning by a Controlled Robot.In Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS, ISBN 978-989-758-479-4, pages 131-140. DOI: 10.5220/0010215201310140

@conference{robovis20,
author={Adil Sarsenov. and Yessenbayeva, A. and Almas Shintemirov. and Adnan Yazici.},
title={Detection of Objects and Trajectories in Real-time using Deep Learning by a Controlled Robot},
booktitle={Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,},
year={2020},
pages={131-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010215201310140},
isbn={978-989-758-479-4},
}

TY - CONF

JO - Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,
TI - Detection of Objects and Trajectories in Real-time using Deep Learning by a Controlled Robot
SN - 978-989-758-479-4
AU - Sarsenov, A.
AU - Yessenbayeva, A.
AU - Shintemirov, A.
AU - Yazici, A.
PY - 2020
SP - 131
EP - 140
DO - 10.5220/0010215201310140

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.