A Compendium of Autonomous Navigation using Object Detection and Tracking in Unmanned Aerial Vehicles

• URL: http://arxiv.org/abs/2506.05378v1
• Date: Sat, 31 May 2025 09:13:43 GMT
• Title: A Compendium of Autonomous Navigation using Object Detection and Tracking in Unmanned Aerial Vehicles
• Authors: Mohit Arora, Pratyush Shukla, Shivali Chopra,
• Abstract summary: Unmanned Aerial Vehicles (UAVs) are one of the most revolutionary inventions of 21st century.<n>This paper attempts to review the various approaches several authors have proposed for the purpose of autonomous navigation of UAVs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned Aerial Vehicles (UAVs) are one of the most revolutionary inventions of 21st century. At the core of a UAV lies the central processing system that uses wireless signals to control their movement. The most popular UAVs are quadcopters that use a set of four motors, arranged as two on either side with opposite spin. An autonomous UAV is called a drone. Drones have been in service in the US army since the 90's for covert missions critical to national security. It would not be wrong to claim that drones make up an integral part of the national security and provide the most valuable service during surveillance operations. While UAVs are controlled using wireless signals, there reside some challenges that disrupt the operation of such vehicles such as signal quality and range, real time processing, human expertise, robust hardware and data security. These challenges can be solved by programming UAVs to be autonomous, using object detection and tracking, through Computer Vision algorithms. Computer Vision is an interdisciplinary field that seeks the use of deep learning to gain a high-level understanding of digital images and videos for the purpose of automating the task of human visual system. Using computer vision, algorithms for detecting and tracking various objects can be developed suitable to the hardware so as to allow real time processing for immediate judgement. This paper attempts to review the various approaches several authors have proposed for the purpose of autonomous navigation of UAVs by through various algorithms of object detection and tracking in real time, for the purpose of applications in various fields such as disaster management, dense area exploration, traffic vehicle surveillance etc.

Related papers

• Cooperative Search and Track of Rogue Drones using Multiagent Reinforcement Learning [8.775925011558995]
  This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities.<n>A holistic system that can reliably detect, track, and neutralize rogue drones is proposed.
  arXiv  Detail & Related papers  (2025-01-07T16:22:51Z)
• A Cross-Scene Benchmark for Open-World Drone Active Tracking [54.235808061746525]
  Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.<n>We propose a unified cross-scene cross-domain benchmark for open-world drone active tracking called DAT.<n>We also propose a reinforcement learning-based drone tracking method called R-VAT.
  arXiv  Detail & Related papers  (2024-12-01T09:37:46Z)
• Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors [6.03212980984729]
  Unmanned Aerial Vehicles (UAVs) are gaining popularity in civil and military applications. prevention and detection of UAVs are pivotal to guarantee confidentiality and safety. This paper presents F-UAV-D (Fast Unmanned Aerial Vehicle Detector), an embedded system that enables fast-moving drone detection.
  arXiv  Detail & Related papers  (2024-03-18T15:27:58Z)
• Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System [56.51247807483176]
  Unmanned Aerial Vehicles (UAVs) have been widely used in many areas, including transportation, surveillance, and military. Previous works have simplified such an anti-UAV task as a tracking problem, where prior information of UAVs is always provided. In this paper, we first formulate a new and practical anti-UAV problem featuring the UAVs perception in complex scenes without prior UAVs information.
  arXiv  Detail & Related papers  (2023-06-27T19:30:23Z)
• Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing [52.50284630866713]
  Existing systems often require hand-engineered components for state estimation, planning, and control. This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies.
  arXiv  Detail & Related papers  (2022-10-26T19:03:17Z)
• Object Detection and Tracking with Autonomous UAV [0.3044887242295643]
  The rotary wing UAV is successfully performed various tasks such as locking on the targets, tracking, and sharing the relevant data with surrounding vehicles. Various software technologies such as API communication, ground control station configuration, autonomous movement algorithms, computer vision, and deep learning are employed.
  arXiv  Detail & Related papers  (2022-06-26T18:48:59Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification [21.48667873335246]
  Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single camera. The coverage of a single camera is limited, necessitating the need for multicamera configurations to match UAVs across cameras. We propose the first new UAV re-identification data set, UAV-reID, that facilitates the development of machine learning solutions in this emerging area.
  arXiv  Detail & Related papers  (2021-04-13T14:13:09Z)
• Artificial Intelligence for UAV-enabled Wireless Networks: A Survey [72.10851256475742]
  Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Artificial intelligence (AI) is growing rapidly nowadays and has been very successful. We provide a comprehensive overview of some potential applications of AI in UAV-based networks.
  arXiv  Detail & Related papers  (2020-09-24T07:11:31Z)
• Detection and Tracking Meet Drones Challenge [131.31749447313197]
  This paper presents a review of object detection and tracking datasets and benchmarks, and discusses the challenges of collecting large-scale drone-based object detection and tracking datasets with manual annotations. We describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions.
  arXiv  Detail & Related papers  (2020-01-16T00:11:56Z)
• Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach [36.587096293618366]
  An emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings. This paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets.
  arXiv  Detail & Related papers  (2020-01-13T23:23:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.