Benchmarking Object Detection and Tracking for UAVs: An Algorithmic Comparison

Object detection and tracking tasks have been pursued by many researchers for a very long time, from traditional computer vision techniques to advanced deep learning architectures. Various object detection and tracking models have been developed for unmanned aerial vehicle (UAV) applications. However, to our knowledge, no study has yet provided a comparative analysis of existing detection and tracking models and their combinations. In this study, our focus is on implementing various object detection and tracking models for edge device deployment in UAVs. Developing detectors for unmanned aerial vehicle platforms is still a difficult undertaking, and no study has compared these models with UAV and edge feasibility in perspective. In this paper, we combined various object detection algorithms with different multi-class multi-object trackers to track multiple targets from the video feed and test performance on edge devices. With this comparison, we achieved a comprehensive analysis of current state-of-the-art tracking and detection algorithms that best suit UAV use cases with different applications.