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.
An improved obstacle separation method using deep learning for object detection and tracking in a hybrid visual control loop for fruit picking in clusters