Explore the vast range of titles available.

A feasibility study was conducted to investigate the use of a wearable gait analysis system for classifying gait speed using a low-cost wearable camera in a semi-structured indoor setting. Data were collected from 19 participants who wore the system during indoor walk sequences at varying self-determined speeds (slow, medium, and fast). Gait parameters using this system were compared with parameters obtained from a vest comprising of a single triaxial accelerometer and from a marker-based optical motion-capture system. Computer-vision techniques and signal processing methods were used to generate frequency-domain gait parameters from each gait-recording device, and those parameters were analysed to determine the effectiveness of the different measurement systems in discriminating gait speed. Results indicate that the authors’ low-cost, portable, vision-based system can be effectively used for in-home gait analysis.

This study develops a vision-based technique for enhancing taillight recognition in autonomous vehicles, aimed at improving real-time decision making by analyzing the driving behaviors of vehicles ahead. The approach utilizes a convolutional 3D neural network (C3D) with feature simplification to classify taillight images into eight distinct states, adapting to various environmental conditions. The problem addressed is the variability in environmental conditions that affect the performance of vision-based systems. Our objective is to improve the accuracy and generalizability of taillight signal recognition under different conditions. The methodology involves using a C3D model to analyze video sequences, capturing both spatial and temporal features. Experimental results demonstrate a significant improvement in the model′s accuracy (85.19%) and generalizability, enabling precise interpretation of preceding vehicle maneuvers. The proposed technique effectively enhances autonomous vehicle navigation and safety by ensuring reliable taillight state recognition, with potential for further improvements under nighttime and adverse weather conditions. Additionally, the system reduces latency in signal processing, ensuring faster and more reliable decision making directly on the edge devices installed within the vehicles.

Parkinson’s disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have been able to analyze its progression to perform stage classification of the disease. Moreover, despite the increasing popularity of these systems for gait analysis, the amount of available gait-related data can often be limited, thereby, hindering the progress of the implementation of this technology in the medical field. As such, creating a quantitative prognosis method that can identify the severity levels of a Parkinsonian gait with little data could help facilitate the study of the Parkinsonian gait for rehabilitation. In this contribution, we propose a vision-based system to analyze the Parkinsonian gait at various stages using linear interpolation of Parkinsonian gait models. We present a comparison between the performance of a k-nearest neighbors algorithm (KNN), support-vector machine (SVM) and gradient boosting (GB) algorithms in classifying well-established gait features. Our results show that the proposed system achieved 96–99% accuracy in evaluating the prognosis of Parkinsonian gaits.

Remote areas often lack access to reliable power, internet, and surveillance infrastructure, making them vulnerable to threats such as illegal intrusion, poaching, and environmental risks. To address these challenges, the propose a self‐sufficient, edge‐AI‐based surveillance system capable of real‐time monitoring, detection, and alerting without relying on cloud connectivity. The system deploys Vision Surveillance Motes equipped with cameras, motion sensors, and acoustic inputs, and uses lightweight artificial intelligence models (MobileNet‐SSD for vision and support vector machines for sound) processed locally on Raspberry Pi boards. Long‐range wireless communication is enabled via LoRa (Long Range) modules, transmitting alerts to a Control Room Mote that displays data using a human‐machine interface (HMI) and pushes updates to a cloud server for optional remote access. This multimodal architecture allows the system to operate in completely offline environments, with optional cloud integration for centralized visibility. The solution is field‐tested and optimized for deployment in forests, disaster‐prone zones, border areas, and rural locations requiring independent surveillance. This study explores the use of aspect tool generation in agriculture for real‐time surveillance and tracking using imaginative prescient and long‐range (LoRa) technology. This study focuses on the deployment of a Control Room and Vision Surveillance Mote at remote locations to address issues with real‐time tracking capabilities and limited connectivity. The innovative Edge‐based fully Vision Technology Enabled Security Surveillance System for Remote Locations uses TensorFlow Lite, OpenCV, PyTorch, and other frameworks to process sensor data in real‐time for quick identification and alerting. The Vision Surveillance Mote sends signals to the Control Room Mote, which serves as the central hub for monitoring and alert management. The Control Room Mote then displays alerts at the HMI display and sends data to a cloud server for remote tracking using internet and cell applications. This system provides real‐time tracking, event detection, and alerting capabilities, offering a comprehensive remote security surveillance solution. The aspect‐based methodology ensures powerful support use while maintaining connectivity in isolated settings.

Portioning large fish fillets for packaging is usually performed manually by skilled workers. Automating this process and obtaining packaged products with attractive shapes and affordable weights will be beneficial for promoting purchase decisions. Towards developing an automated fish fillet portioning system, this study investigated a computer vision approach for determining the fillet volume. A belt conveyor would transport a fish fillet to the image capture booth, where its cross-section areas would be calculated for volume determination. The developed system could be operated with a conveyor speed ranging from 7.5 to 30.6 mm/s. The system performance was evaluated at a conveyor speed of 7.5 mm/s using small catfish fillets from 142.2 to 225.4 cm3. A mean percent error of 9.2% was observed, and the smallest percent error of 3.8% was obtained with a 225.4 cm3 fillet. With minor measurement errors obtained for larger fillets, the proposed computer vision system showed great potential for developing a cost-effective automated system for customized fish fillet partitioning to accelerate purchase decisions and also for quality control and classification of the fish fillets.

Accurate indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GPS-denied environments, especially for small-object detection and under low-light conditions. We propose Robust Wavelet-Aware YOLO (RWA-YOLO), a vision-based detection framework that integrates a wavelet-aware attention fusion module with a dual multi-path aggregation mechanism to enhance small-object detection and multi-scale feature representation. UAV-mounted LEDs are utilized to ensure robust visual perception in low-light indoor scenarios. The UAV’s three-dimensional position is estimated through multi-view geometric triangulation without relying on external beacons or artificial markers. Beyond static localization, the system is validated under dynamic flight conditions, demonstrating smooth and temporally coherent trajectory reconstruction suitable for real-time control loops (update rate ≈25FPS). Extensive experiments in real indoor environments achieve centimeter-level localization accuracy (root mean square error: 9.9 mm, 95th percentile error: 13.5 mm), outperforming state-of-the-art vision-based methods and achieving accuracy comparable to or better than representative hybrid ultra-wideband–vision systems reported in the literature. These results confirm the effectiveness, robustness, and real-time capability of RWA-YOLO for indoor UAV navigation in constrained environments.

We propose a new flexible technique for accurate vision-based seismic displacement measurement of building structures via a single non-stationary camera with any perspective view. No a priori information about the camera’s parameters or only partial knowledge of the internal camera parameters is required, and geometric constraints in the world coordinate system are employed for projective rectification in this research. Whereas most projective rectifications are conducted by specifying the positions of four or more fixed reference points, our method adopts a stratified approach to partially determine the projective transformation from line-based geometric relationships on the world plane. Since line features are natural and plentiful in a man-made architectural building environment, robust estimation techniques for automatic projective/affine distortion removal can be applied in a more practical way. Both simulations and real-recorded data were used to verify the effectiveness and robustness of the proposed method. We hope that the proposed method could advance the consumer-grade camera system for vision-based structural measurement one more step, from laboratory environments to real-world structural health monitoring systems.

A continuous welded rail (CWR) is a critical component of modern rail systems, providing increased stability, improved passenger comfort, and reduced maintenance compared with jointed rails. However, the unique mechanical properties of CWR, particularly in the immovable zone where friction restricts longitudinal deformation, require accurate and continuous monitoring to prevent rail buckling or fractures. Despite the availability of various strain‐monitoring technologies, including fiber Bragg grating sensors, strain gauges, and vision‐based systems, these approaches have significant limitations in full‐scale CWR applications. Challenges such as insufficient resolution for detecting minute strains and sensor‐adhesion durability reduce the effectiveness of current strain‐monitoring solutions. To address these limitations, we propose a high‐resolution, vision‐based biaxial strain measurement system specifically designed for CWRs. This system utilizes three microscopic cameras strategically positioned to capture detailed displacement data, allowing for accurate computation of biaxial strain through advanced image processing techniques. The proposed system was validated through both laboratory‐scale and full‐scale experiments and exhibited a minimum detectable strain of 1.5 µε under controlled loading conditions.

Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications.

In recent years, the intensity and frequency of fires have increased significantly, resulting in considerable damage to properties and the environment through wildfires, oil pipeline fires, hazardous gas emissions, and building fires. Effective fire management systems are essential for early detection, rapid response, and mitigation of fire impacts. To address this challenge, unmanned aerial vehicles (UAVs) integrated with advanced state-of-the-art deep learning techniques offer a transformative solution for real-time fire detection, monitoring, and response. As UAVs play an essential role in the detection, classification and segmentation of fire-affected regions, enhancing vision-based fire management through advanced computer vision and deep learning technologies. This comprehensive survey critically examines recent advancements in vision-based fire management systems enabled by autonomous UAVs. It explores how baseline deep learning models, including convolutional neural networks, attention mechanisms, YOLO variants, generative adversarial networks and transformers, enhance UAV capabilities for fire-related tasks. Unlike previous reviews that focus on conventional machine learning and general AI approaches, this survey emphasizes the unique advantages and applications of deep learning-driven UAV platforms in fire scenarios. It provides detailed insights into various architectures, performance and applications used in UAV-based fire management. Additionally, the paper provides detailed insights into the available fire datasets along with their download links and outlines critical challenges, including data imbalance, privacy concerns, and real-time processing limitations. Finally, the survey identifies promising future directions, including multimodal sensor fusion, lightweight neural network architectures optimized for UAV deployment, and vision-language models. By synthesizing current research and identifying future directions, this survey aims to support the development of robust, intelligent UAV-based solutions for next-generation fire management. Researchers and professionals can access the GitHub repository.