Abstract

Currently, deployment of UAV has transformed from crucial to day-to-day scenarios for various purposes such as wastage collection, live entertainment, product delivery, town mapping, etc. Object tracking based UAV applications such as traffic monitoring, wildlife monitoring and surveillance have undergone phenomenal changeover due to deep learning based methodologies. With such transformation, there is also lack of resources to practically explore the UAV images and videos with deep learning methodologies. Hence, a deep learning-based object detection and tracking tool with UAV data (DL-ODT-UAV) is proposed to fill the learning gap, especially among students. DL-ODT-UAV is a resource to acquire basic knowledge about UAV and deep learning based object detection and tracking. It integrates various object annotators, object detectors and object tracker. Single object detection and tracking is performed with YOLO as object detector and LSTM as object tracker. Faster R-CNN is adopted in multiple object detection. With exploring the tool, the ability of students to approach problems related to deep learning methodologies will improve to a greater level.

Details

Title
A TOOL TO ENHANCE THE CAPACITY FOR DEEP LEARNING BASED OBJECT DETECTION AND TRACKING WITH UAV DATA
Author
Micheal, A A 1 ; Vani, K 1 ; Sanjeevi, S 2 ; C-H, Lin 3 

 Dept. of Information Science and Technology, College of Engineering, Anna University, Chennai, India; Dept. of Information Science and Technology, College of Engineering, Anna University, Chennai, India 
 Dept. of Geology, College o f Engineering, Anna University, Chennai, India; Dept. of Geology, College o f Engineering, Anna University, Chennai, India 
 Dept. of Geomatics, National Cheng-Kung University, No. 1, University Road, Tainan City 701, Taiwan; Dept. of Geomatics, National Cheng-Kung University, No. 1, University Road, Tainan City 701, Taiwan 
Pages
221-226
Publication year
2020
Publication date
2020
Publisher
Copernicus GmbH
ISSN
16821750
e-ISSN
21949034
Source type
Conference Paper
Language of publication
English
ProQuest document ID
2436794369
Copyright
© 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.