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Object Detection Tracking and Action Recognition in Dynamic and Unconstrained Environments

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 3720

Special Issue Editors


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Guest Editor
Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, Croatia
Interests: pattern recognition; computer vision; soft computing; deep learning; image classification; object detection and tracking; action recognition; drone images; thermal images

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Guest Editor
Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, Croatia
Interests: computer vision; deep learning; image classification; object detection and tracking; action recognition; action localization

Special Issue Information

Dear Colleagues, 

The ability to detect, track, and locate objects in videos and to recognize events and actions performed by objects in dynamic and unconstrained environments is a fundamental challenge in computer vision. With the increasing availability of cameras and sensors, vision-based systems are being used in a wide range of applications, including surveillance, autonomous vehicles, robotics, and augmented reality. However, these applications often require real-time and robust object detection and tracking or recognition of actions and events in complex environments with various lighting conditions, occlusions, and dynamic backgrounds.

This Special Issue of the journal focuses on recent advances in vision-based object detection and tracking such as action and event recognition in dynamic and unconstrained environments. It aims to bring together researchers and practitioners from academia and industry to present and discuss their latest research results, challenges, and future directions in this field.

We invite original research papers, reviews, and case studies that address, but are not limited to, the following topics:

  • Object detection in dynamic and unconstrained environments;
  • Object tracking in complex scenes with occlusions and dynamic backgrounds;
  • Object localization in videos;
  • Recognition of actions and events in complex and unconstrained environments;
  • Deep learning-based approaches for object detection tracking and action recognition;
  • Multi-camera and sensor fusion for robust object tracking;
  • Real-time and low-latency object detection and tracking;
  • Large-scale datasets and benchmarks for object detection and tracking;
  • Applications of vision-based object detection and tracking, such as surveillance, autonomous vehicles, robotics, and augmented reality.

Dr. Marina Ivasic-Kos
Dr. Miran Pobar
Guest Editors

Manuscript Submission Information

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Keywords

  • autonomous drones and application of autonomous drones
  • development of AI algorithms for specific drone tasks
  • computer vision for autonomous drones
  • visible/infrared thermal/multispectral image analysis
  • using drones for autonomous monitoring and inspection
  • using drones for object detection, recognition, and tracking
  • large-scale drone datasets for training and testing deep learning solutions
  • connected drones
  • fleet management of multiple drones, flight planning for the fleet of drones
  • AR and drones
  • 5G wireless transmission and drones

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Published Papers (2 papers)

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Research

24 pages, 46034 KiB  
Article
Immersive Robot Teleoperation Based on User Gestures in Mixed Reality Space
by Hibiki Esaki and Kosuke Sekiyama
Sensors 2024, 24(15), 5073; https://doi.org/10.3390/s24155073 - 5 Aug 2024
Viewed by 1627
Abstract
Recently, research has been conducted on mixed reality (MR), which provides immersive visualization and interaction experiences, and on mapping human motions directly onto a robot in a mixed reality (MR) space to achieve a high level of immersion. However, even though the robot [...] Read more.
Recently, research has been conducted on mixed reality (MR), which provides immersive visualization and interaction experiences, and on mapping human motions directly onto a robot in a mixed reality (MR) space to achieve a high level of immersion. However, even though the robot is mapped onto the MR space, their surrounding environment is often not mapped sufficiently; this makes it difficult to comfortably perform tasks that require precise manipulation of the objects that are difficult to see from the human perspective. Therefore, we propose a system that allows users to operate a robot in real space by mapping the task environment around the robot on the MR space and performing operations within the MR space. Full article
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32 pages, 2692 KiB  
Article
Feature Detection of Non-Cooperative and Rotating Space Objects through Bayesian Optimization
by Rabiul Hasan Kabir and Xiaoli Bai
Sensors 2024, 24(15), 4831; https://doi.org/10.3390/s24154831 - 25 Jul 2024
Viewed by 1247
Abstract
In this paper, we propose a Bayesian Optimization (BO)-based strategy using the Gaussian Process (GP) for feature detection of a known but non-cooperative space object by a chaser with a monocular camera and a single-beam LIDAR in a close-proximity operation. Specifically, the objective [...] Read more.
In this paper, we propose a Bayesian Optimization (BO)-based strategy using the Gaussian Process (GP) for feature detection of a known but non-cooperative space object by a chaser with a monocular camera and a single-beam LIDAR in a close-proximity operation. Specifically, the objective of the proposed Space Object Chaser-Resident Assessment Feature Tracking (SOCRAFT) algorithm is to determine the camera directional angles so that the maximum number of features within the camera range is detected while the chaser moves in a predefined orbit around the target. For the chaser-object spatial incentive, rewards are assigned to the chaser states from a combined model with two components: feature detection score and sinusoidal reward. To calculate the sinusoidal reward, estimated feature locations are required, which are predicted by Gaussian Process models. Another Gaussian Process model provides the reward distribution, which is then used by the Bayesian Optimization to determine the camera directional angles. Simulations are conducted in both 2D and 3D domains. The results demonstrate that SOCRAFT can generally detect the maximum number of features within the limited camera range and field of view. Full article
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