The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B5-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 221–226, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-221-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 221–226, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-221-2020

  24 Aug 2020

24 Aug 2020

A TOOL TO ENHANCE THE CAPACITY FOR DEEP LEARNING BASED OBJECT DETECTION AND TRACKING WITH UAV DATA

A. A. Micheal1, K. Vani1, S. Sanjeevi2, and C.-H. Lin3 A. A. Micheal et al.
  • 1Dept. of Information Science and Technology, College of Engineering, Anna University, Chennai, India
  • 2Dept. of Geology, College o f Engineering, Anna University, Chennai, India
  • 3Dept. of Geomatics, National Cheng-Kung University, No. 1, University Road, Tainan City 701, Taiwan

Keywords: UAV, Deep Learning, Object Detection and Tracking

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.