A TOOL to ENHANCE the CAPACITY for DEEP LEARNING BASED OBJECT DETECTION and TRACKING with UAV DATA

A. A. Micheal, K. Vani, S. Sanjeevi, Chao Hung Lin

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Pages (from-to)221-226
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume43
Issue numberB5
DOIs
Publication statusPublished - 2020 Aug 6
Event2020 24th ISPRS Congress - Technical Commission V (TC-V) on Education and Outreach - Youth Forum - Nice, Virtual, France
Duration: 2020 Aug 312020 Sep 2

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development

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