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Vision Sensors for Object Detection and Tracking
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Vision Sensors for Object Detection and Tracking

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 7118

Special Issue Editor

Prof. Dr. ByoungChul Ko

E-Mail Website
Guest Editor
Department of Computer Engineering, Keimyung University, Shindang-Dong, Dalseo-Gu, Daegu 704-701,Republic of Korea
Interests: computer vision; pattern recognition; object detection tracking; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Object detection and tracking are fundamental tasks in computer vision and have witnessed remarkable progress in recent years, driven by the surge in deep learning techniques. With the proliferation of cameras and sensors, vision-based systems are increasingly being deployed in a wide range of applications, including surveillance, autonomous driving, robotics, and augmented reality. However, these applications often require robust real-time object detection and tracking, and even action and event recognition, in challenging environments with varying lighting conditions, occlusions, and dynamic backgrounds. This Special Issue invites researchers to present their latest research findings, address existing challenges, and explore future directions in vision-sensor-based object detection and tracking, to pave the way for advancements in surveillance, autonomous driving, and other critical application areas.

  • Deep-learning-based object detection and tracking in videos;
  • Object tracking in low-resolution and low-light conditions;
  • Long-tailed object detection and tracking;
  • Real-time object detection and tracking;
  • Object detection and tracking in 3D videos;
  • Multi-object tracking in complex scenes;
  • Video object segmentation in complex environments;
  • Applications in surveillance, autonomous driving, and robotics;

Prof. Dr. ByoungChul Ko
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • object detection tracking
  • deep learning
  • computer vision

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Published Papers (5 papers)

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Research

25 pages, 8373 KiB  
Article
Efficacy of Segmentation for Hyperspectral Target Detection
by Yoram Furth and Stanley R. Rotman
Sensors 2025, 25(1), 272; https://doi.org/10.3390/s25010272 - 6 Jan 2025
Viewed by 755
Abstract
Algorithms for detecting point targets in hyperspectral imaging commonly employ the spectral inverse covariance matrix to whiten inherent image noise. Since data cubes often lack stationarity, segmentation appears to be an attractive preprocessing operation. Surprisingly, the literature reports both successful and unsuccessful segmentation [...] Read more.
Algorithms for detecting point targets in hyperspectral imaging commonly employ the spectral inverse covariance matrix to whiten inherent image noise. Since data cubes often lack stationarity, segmentation appears to be an attractive preprocessing operation. Surprisingly, the literature reports both successful and unsuccessful segmentation cases, with no clear explanations for these divergent outcomes. This paper elucidates the conditions under which segmentation might improve detector performance. Focusing on a representative algorithm and assuming a target additive model, the study examines all influential factors through theoretical analysis and extensive simulations. The findings offer fundamental insights and practical guidelines for characterizing segmented datasets, enabling a thorough evaluation of segmentation’s utility for detector performance. They outline the range of target scenarios and parameters where segmentation may prove beneficial and help assess the potential impact of proposed segmentation strategies on detection outcomes. Full article
(This article belongs to the Special Issue Vision Sensors for Object Detection and Tracking)
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20 pages, 2772 KiB  
Article
Activities of Daily Living Object Dataset: Advancing Assistive Robotic Manipulation with a Tailored Dataset
by Md Tanzil Shahria and Mohammad H. Rahman
Sensors 2024, 24(23), 7566; https://doi.org/10.3390/s24237566 - 27 Nov 2024
Cited by 1 | Viewed by 1010
Abstract
The increasing number of individuals with disabilities—over 61 million adults in the United States alone—underscores the urgent need for technologies that enhance autonomy and independence. Among these individuals, millions rely on wheelchairs and often require assistance from another person with activities of daily [...] Read more.
The increasing number of individuals with disabilities—over 61 million adults in the United States alone—underscores the urgent need for technologies that enhance autonomy and independence. Among these individuals, millions rely on wheelchairs and often require assistance from another person with activities of daily living (ADLs), such as eating, grooming, and dressing. Wheelchair-mounted assistive robotic arms offer a promising solution to enhance independence, but their complex control interfaces can be challenging for users. Automating control through deep learning-based object detection models presents a viable pathway to simplify operation, yet progress is impeded by the absence of specialized datasets tailored for ADL objects suitable for robotic manipulation in home environments. To bridge this gap, we present a novel ADL object dataset explicitly designed for training deep learning models in assistive robotic applications. We curated over 112,000 high-quality images from four major open-source datasets—COCO, Open Images, LVIS, and Roboflow Universe—focusing on objects pertinent to daily living tasks. Annotations were standardized to the YOLO Darknet format, and data quality was enhanced through a rigorous filtering process involving a pre-trained YOLOv5x model and manual validation. Our dataset provides a valuable resource that facilitates the development of more effective and user-friendly semi-autonomous control systems for assistive robots. By offering a focused collection of ADL-related objects, we aim to advance assistive technologies that empower individuals with mobility impairments, addressing a pressing societal need and laying the foundation for future innovations in human–robot interaction within home settings. Full article
(This article belongs to the Special Issue Vision Sensors for Object Detection and Tracking)
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21 pages, 2544 KiB  
Article
An Energy-Efficient Dynamic Feedback Image Signal Processor for Three-Dimensional Time-of-Flight Sensors
by Yongsoo Kim, Jaehyeon So, Chanwook Hwang, Wencan Cheng and Jong Hwan Ko
Sensors 2024, 24(21), 6918; https://doi.org/10.3390/s24216918 - 28 Oct 2024
Cited by 1 | Viewed by 1153
Abstract
With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively [...] Read more.
With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices. Full article
(This article belongs to the Special Issue Vision Sensors for Object Detection and Tracking)
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20 pages, 3099 KiB  
Article
Diffusion Models-Based Purification for Common Corruptions on Robust 3D Object Detection
by Mumuxin Cai, Xupeng Wang, Ferdous Sohel and Hang Lei
Sensors 2024, 24(16), 5440; https://doi.org/10.3390/s24165440 - 22 Aug 2024
Cited by 2 | Viewed by 1297
Abstract
LiDAR sensors have been shown to generate data with various common corruptions, which seriously affect their applications in 3D vision tasks, particularly object detection. At the same time, it has been demonstrated that traditional defense strategies, including adversarial training, are prone to suffering [...] Read more.
LiDAR sensors have been shown to generate data with various common corruptions, which seriously affect their applications in 3D vision tasks, particularly object detection. At the same time, it has been demonstrated that traditional defense strategies, including adversarial training, are prone to suffering from gradient confusion during training. Moreover, they can only improve their robustness against specific types of data corruption. In this work, we propose LiDARPure, which leverages the powerful generation ability of diffusion models to purify corruption in the LiDAR scene data. By dividing the entire scene into voxels to facilitate the processes of diffusion and reverse diffusion, LiDARPure overcomes challenges induced from adversarial training, such as sparse point clouds in large-scale LiDAR data and gradient confusion. In addition, we utilize the latent geometric features of a scene as a condition to assist the generation of diffusion models. Detailed experiments show that LiDARPure can effectively purify 19 common types of LiDAR data corruption. Further evaluation results demonstrate that it can improve the average precision of 3D object detectors to an extent of 20% in the face of data corruption, much higher than existing defence strategies. Full article
(This article belongs to the Special Issue Vision Sensors for Object Detection and Tracking)
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17 pages, 7257 KiB  
Article
Integrating Heuristic Methods with Deep Reinforcement Learning for Online 3D Bin-Packing Optimization
by Ching-Chang Wong, Tai-Ting Tsai and Can-Kun Ou
Sensors 2024, 24(16), 5370; https://doi.org/10.3390/s24165370 - 20 Aug 2024
Cited by 1 | Viewed by 2153
Abstract
This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement learning are integrated to implement this method. In the heuristic [...] Read more.
This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement learning are integrated to implement this method. In the heuristic algorithms for bin-packing, an extreme point priority sorting method is proposed to sort the generated extreme points according to their waste spaces to improve space utilization. In addition, a 3D grid representation of the space status of the container is used, and some partial support constraints are proposed to increase the possibilities for stacking objects and enhance overall space utilization. In the PPO algorithm, some heuristic algorithms are integrated, and the reward function and the action space of the policy network are designed so that the proposed method can effectively complete the online 3D bin-packing task. Some experimental results illustrate that the proposed method has good results in achieving online 3D bin-packing tasks in some simulation environments. In addition, an environment with image vision is constructed to show that the proposed method indeed enables an actual robot manipulator to successfully and effectively complete the bin-packing task in a real environment. Full article
(This article belongs to the Special Issue Vision Sensors for Object Detection and Tracking)
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