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5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Wang, Xuli Smith, Melvyn Zook, Nancy Conway, Myra Zhang, Wenhao Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom School of Social Sciences University of the West of England Bristol United Kingdom College of Health Psychology and Social Care University of Derby Derby United Kingdom

ISBN: (纸本)9789819638628

Eye tracking has been increasingly recognised as a valuable tool for assessing cognitive health. However, many existing eye tracking systems are often expensive, imprecise, and inconvenient, which limits their accessibility and effectiveness in clinical settings. To address these challenges, we propose a novel deep learning-based method for pupil centre and eye corner localisation using a single camera, integrating a Nested UNet, Gaussian heatmaps, Top-K averaging, and Adaptive Wing Loss. We also present a bespoke proof-of-concept hardware system designed to be compatible with our eye-tracking method, making it well-suited for conveniently administering eye-tracking tests for cognitive health monitoring. Our eye tracking model demonstrated superior localisation results on the benchmark GI4E dataset, when compared with other state-of-the-art methods, by achieving an accuracy of 99.11% with a tolerance of 0.025 normalised error. Additionally, preliminary qualitative results exhibited outstanding generalisation performance on a dataset captured by the proof-of-concept hardware system. The proposed eye tracking model and hardware system will be used in future work to develop accessible, user-friendly, and cost-effective solutions for cognitive health monitoring and disease diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Towards AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom University of West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Hartpury University Department of Animal and Agriculture Gloucester United Kingdom Aston University College of Engineering and Physical Sciences Birmingham United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating Artificial Intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model's effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model's capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision-making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step towards recognising the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2025 IEEE.

关键词： Artificial Intelligence Autonomous Vehicles Deep leaning Edge Computing Internet of Vehicles

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence Computer architecture Data models Accuracy Servers Safety

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Quadruped locomotion analysis using three-dimensional video 1

Quadruped locomotion analysis using three-dimensional video

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1st International Conference for Students on Applied Engineering, ICSAE 2016

作者： Abdul Jabbar, K. Hansen, M.F. Smith, M.L. Smith, L.N. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom

ISBN: (纸本)9781467390538

To date, there has not been a single method suitable for large-scale or regular-basis implementation to analyze the locomotion of quadruped animals. Existing methods are not sensitive enough for detecting minor deviations from healthy gaits. That is important because these minor deviations could develop into a severe painful lameness condition. We introduce a dynamic novel proxy for early stage lameness by analyzing the height movements from an overhead-view 3D video data. These movements are derived from key regions (e.g. spine, hook joints, and sacroiliac joint). The features to these key regions are automatically tracked using shape index and curvedness threshold from the 3D map. Our system is fully automated, covert and non-intrusive. This directly affects the accuracy of the analysis as we are able to observe the animals without spooking them. We believe that our proposed method could be used on other animals, i.e. predator quadrupeds where human presence is difficult. © 2016 School of Mechanical and Systems Engineering.

关键词： Animals

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Time-delay compensation and disturbance rejection in the control of robots with visco-elastic joints using loop-shaping approach

Time-delay compensation and disturbance rejection in the con...

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2007 IEEE International Workshop on Robotic and Sensor Environments, ROSE 2007

作者： Torabi, Meysam Taher, Mahdieh Ardekani, Reza Dehestani Jahed, Mehran Sharif University of Technology Robotics and Machine Vision Laboratory Amirkabir University of Technology Research and Development of Advanced Software System Laboratory

ISBN: (纸本)1424415276

In many medical applications, it is necessary to precisely control a robot to achieve exact positioning. In such cases, some components of robot which had been considered rigid should be modeled with visco-elastic elements to present more exact model of robot, and hence controllers designed under the assumption of rigidity may not accurately control them, especially when time-delay and disturbances have been appeared in the closed-loop system. In this study, we present a new control approach to force such robots to have rigid and exact motions, while they have visco-elastic components. Time-delay in the feedback path defects transient state response, while disturbances change the steady state response. Therefore different controllers are required to correct the robot behaviors. In this regard, robot dynamics is extracted and converted to the characteristic equation. Then, by an appropriate system identification method, an order-reduced model is obtained. Loop-shaping technique is exploited on the magnitude frequency response to design different model-based controllers for principal zones of the frequency response. The simulation results illustrate appropriate time-delay compensation and disturbance rejection in the time response. ©2007 IEEE.

关键词： Disturbance rejection

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Multiscale Convolutional Transformer with Diverse-aware Feature Learning for Motor Imagery EEG Decoding

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hang, Wenlong Wang, Junliang Liang, Shuang Lei, Baiying Wang, Qiong Li, Guanglin Chen, Badong Qin, Jing Nanjing Tech University College of Computer Nanjing211816 China Nanjing Tech University Information Engineering Nanjing211816 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210023 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China Guangdong Provincial Key Laboratory of Computer Vision Shenzhen518055 China Virtual Reality Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences Shenzhen518055 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hong Kong Polytechnic University School of Nursing Hung Hom Hong Kong

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) have significant potential in improving motor function for neurorehabilitation. Despite recent advancements, learning diversified EEG features across different frequency ranges remains a significant challenge, as the homogenization of feature representations often limits the generalization capabilities of EEG decoding models for BCIs. In this paper, we propose a novel multiscale convolutional transformer framework for EEG decoding that integrates multiscale convolution, transformer, and diverse-aware feature learning scheme (MCTD) to tackle the above challenge. Specifically, we first capture multiple frequency features using dynamic one-dimensional temporal convolution with different kernel lengths. Subsequently, we incorporate convolutional layers and transformers with a contrastive learning scheme to extract discriminative local and global EEG features within a single frequency range. To mitigate the homogenization of features extracted from different frequency ranges, we propose a novel decorrelation regularization. It enables multiscale convolutional transformers produce less correlated features with each other, thereby enhancing the overall expressiveness of EEG decoding model. The performance of MCTD is evaluated on four public MI-based EEG datasets, including the BCI competition III 3a and IV 2a, the BNCI 2015- 001, and the OpenBMI. For the average Kappa/Accuracy scores, MCTD obtains improvements of 3.58%/2.68%, 3.09%/2.20%, 2.33%/1.54%, and 4.44%/2.22%, over the state-of-the-art method on four EEG datasets, respectively. Experimental results demonstrate that our method exhibits superior performance. © 2016 IEEE.

关键词： Brain computer interface

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Real-Time Livestock Activity Monitoring via Fine-Tuned Faster R-CNN for Multiclass Cattle Behaviour Detection 14

Real-Time Livestock Activity Monitoring via Fine-Tuned Faste...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Ahmad, Misbah Zhang, Wenhao Smith, Melvyn Brilot, Ben Bell, Matt Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom

ISBN: (纸本)9798350304138

Automated cattle activity detection plays a pivotal role in modern livestock management, significantly impacting animal welfare and operational efficiency. This paper introduces an automated approach for cattle activity detection using advanced deep learning-based architecture named Faster-RCNN. The deep learning model addresses the simultaneous detection and precise localization of three primary cattle activities in the input image: standing, lying, and walking. The methodology involves fine-tuning a pre-trained model using a dataset collected from a real-time barn environment at Hartpury University Farm. Overall, the proposed approach is based on data pre-processing and fine-tuning steps. Data augmentation techniques, such as random cropping, flipping, and rotation, ensure dataset diversity. This enriches the model's generalization ability across various lighting conditions and cattle orientations. The fine-tuning process adapts a pre-trained model, initially trained on a general object detection dataset. We adjust the model's architecture to the subtleties inherent in different cattle activities through training on our custom cattle activity dataset. This process ensures the model's significance in accurately detecting and classifying the distinct behaviours of cattle. Experimental results demonstrate the model's effectiveness in identifying and localizing cattle activities. The model correctly predicted of standing, lying, and walking events with accuracy rate of 0.94, 0.92 and 0.89 respectively. © 2023 IEEE.

关键词： Computer vision

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Inverse dynamics control of needle in prostate brachytherapy

Inverse dynamics control of needle in prostate brachytherapy

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IEEE International Conference on Industrial Technology (ICIT)

作者： Arash Maghsoudi Mehran Jahed Robotics and Machine Vision Laboratory Sharif University of technology Tehran Iran

Needle insertion into the soft tissue has been the subject of many studies during the last decade, while needle control has become a crucial training tool, evaluating surgeon's skills in such critical incision. This study considers a model-based dynamics equation for the needle movement through the soft tissue. In the proposed model, the force distribution along the needle shaft is estimated through the use of tissue deformation data and tissue model. A novel algorithm for the needle control simulation is also proposed based on the developed dynamics equation of the needle movement. To point out the role of mechanical properties of the soft tissue, an inverse dynamics control method is used to demonstrate the system performance in presence of the tissue mechanical parameters uncertainty. It is shown that the uncertainty in the tissue mechanical parameters dramatically affects the system performance. Hence subsequently, a sensitivity analysis is conducted on the controller parameters (gains) and needle velocity indices are introduced to evaluate the performance of the controller. Eventually it is shown that the inverse dynamics controller provides suitable position control.

关键词： Young's modulus Robots Phantoms

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A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation 37

A Single 2D Pose with Context is Worth Hundreds for 3D Human...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Zhao, Qitao Zheng, Ce Liu, Mengyuan Chen, Chen Robotics Institute Carnegie Mellon University United States Center for Research in Computer Vision University of Central Florida United States Key Laboratory of Machine Perception Peking University Shenzhen Graduate School China

ISBN: (纸本)9781713899921

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for improved accuracy, which incurs performance saturation, intractable computation and the non-causal problem. This can be attributed to their inherent inability to perceive spatial context as plain 2D joint coordinates carry no visual cues. To address this issue, we propose a straightforward yet powerful solution: leveraging the readily available intermediate visual representations produced by off-the-shelf (pre-trained) 2D pose detectors - no finetuning on the 3D task is even needed. The key observation is that, while the pose detector learns to localize 2D joints, such representations (e.g., feature maps) implicitly encode the joint-centric spatial context thanks to the regional operations in backbone networks. We design a simple baseline named Context-Aware PoseFormer to showcase its effectiveness. Without access to any temporal information, the proposed method significantly outperforms its context-agnostic counterpart, PoseFormer [74], and other state-of-the-art methods using up to hundreds of video frames regarding both speed and precision. Project page: ***/ContextAware-PoseFormer. © 2023 Neural information processing systems foundation. All rights reserved.

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Vanishing point detection for CCTV in railway stations

Vanishing point detection for CCTV in railway stations

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5th International Conference on Imaging for Crime Detection and Prevention, ICDP 2013

作者： Tarrit, K. Atkinson, G.A. Smith, M.L. Molleda, J. Wright, G.C. Gaal, P. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Department of Computer Science and Engineering University of Oviedo Spain Aralia Systems Ltd. Horsham United Kingdom

ISBN: (纸本)9781849199049

Vanishing point (VP) detection is an important task in computer vision with particular importance in video surveillance. However, despite having numerous applications in camera calibration, 3D reconstruction and threat detection, a general method for VP detection has remained elusive. Rather than attempting the infeasible task of VP detection in general scenes, this paper presents a novel method that is fine-tuned to work for railway station environments and is shown to outperform the state-of-the-art for that particular case. The motivation for this is that (a) these environments are particularly susceptible to many types of crime from petty theft to terrorist activity, (b) the number of objects/structures in the scenes are limited, rendering the problem more tractable than the generic case, and (c) they typically have many CCTV cameras already installed. The method presented here commences by extracting edges from the input frame using the Canny edge detector as a pre-processing stage before the standard Hough transform is employed. A novel line clustering algorithm is then applied to determine the parameters of the lines that converge at VPs. This is based on statistics of the detected lines and heuristics about the type of scene. The clustered lines are then used to compute VPs using their intersection points. A voting system is used to optimise detection in an attempt to omit spurious lines. The paper includes a direct comparison to the state-of-the-art and ground truth.

关键词： Subway stations

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