Search Results (118)

Real-time, precise monitoring of the number and distribution of indoor personnel is crucial for building safety management, operational optimization, and personnel scheduling. However, narrow entrances and high-density passageways often lead to missed detections, false positives, and tracking failures in pedestrian detection, thereby reducing cross-line counting accuracy. Additionally, edge devices deployed in practical scenarios frequently process multiple video streams simultaneously, resulting in computational resource constraints. To address these challenges, this paper proposes a lightweight, enhanced multi-object pedestrian tracking and counting method tailored for indoor scenarios by optimizing deep learning models. Firstly, modular optimizations are applied to the YOLOv8n model to construct a more lightweight detector, RL_YOLOv8, reducing computational overhead while maintaining accuracy. Secondly, correlated pedestrian auxiliary prediction and pedestrian position change constraints are employed to mitigate ID switching, tracking interruptions, and trajectory jumps in dense scenes. Finally, a buffer zone auxiliary counting strategy is designed to further reduce missed detections of pedestrians crossing lines. Experimental results demonstrate that compared to the original detection-and-tracking-based line-crossing counting method, the improved approach effectively enhances counting accuracy and real-time performance, better meeting the requirements of practical intelligent security and crowd monitoring systems. Full article

The Liancheng White Duck is a nationally protected breed in China, but its high-density farming environment poses significant challenges for target detection and behavior recognition, particularly due to occlusion, motion blur, and flock aggregation, making practical flock monitoring and counting labor intensive and prone to error in real barns. To address these issues, we propose DenseDuckMOT, an integrated detection-tracking framework for practical farm monitoring using existing fixed surveillance cameras with minimal additional hardware cost that combines the improved DuckNet detector with the AKFTrack tracker. DuckNet, based on YOLOv11, incorporates BiFPN, GLSA, and ESDH. It achieves high performance with 98.19% precision, 94.79% mAP@0.75, 97.70% F1-score, and 97.72% recall, while maintaining a lightweight design of only 1.90M parameters and a model size of 4485 KB. AKFTrack introduces adaptive Kalman prediction and a two-stage association scheme. It is evaluated on five dense white duck surveillance videos, where it outperforms or ranks second in MOTA, IDF1, and recall compared to DeepSORT, StrongSORT, and ByteTrack, especially in crowded and occluded scenes. Experimental results, ablation studies, and LayerCAM visualizations confirm the complementary advantages of BiFPN, GLSA, and ESDH, as well as the robustness of AKFTrack in handling occlusion and rapid motion. DenseDuckMOT provides accurate, efficient, and stable real-time monitoring in dynamic poultry farms, offering a scalable solution for intelligent farming. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

Congested platforms in public transportation systems can jeopardize the safety and comfort of passengers. Real-time crowd size estimation using Device-Free Wireless Sensing (DFWS) can offer a privacy-preserving solution for monitoring and preventing overcrowding. However, no public dataset exists on DFWS in public transportation environments. In this work, we introduce a new dataset comprising two different public transportation environments, which contains data on the presence of rail vehicles at the platform, as well as manual people counts at regular intervals. By providing this dataset, we aim to offer a foundation for other DFWS researchers to explore novel algorithms and methods in public transportation environments. Full article

Staphylococcus aureus is a common opportunistic pathogen found in various environments, with the potential for rapid spread, especially in densely populated indoor settings. Integrating traditional microbiological monitoring with molecular techniques is critical for the timely detection and control of such pathogens. The aim of this study was (1) to monitor the presence and spread of S. aureus in a crowded occupational environment and (2) to optimize a PCR protocol with sequence specific primers (PCR-SSP) for precise identification and early detection of this microorganism and its antibiotic resistance genes. Sampling was conducted in two different places: a call center and a healthcare facility room. All samples were collected from indoor areas at two different time points (T0 and T1) in May 2025 (mean temperature: 22.5 °C; humidity: 59.5%). Microbiological techniques and molecular analysis using PCR-SSP were employed to confirm the presence of S. aureus and detect antibiotic resistance genes such as mecA. A total CFU (colony-forming unit) count of 587 was recorded at the dental clinic corridor, and a total CFU count of 2008 was recorded at the call center corridor. PCR-SSP successfully confirmed the identity of S. aureus with an amplicon size 267 bp and enabled the detection of antibiotic resistance markers, validating its use as a complementary method to traditional microbiological techniques. This study highlights the importance of combining environmental monitoring with molecular biology tools to enhance the early detection and accurate identification of microbial pathogens such as S. aureus and provide an insight for our future direction of producing biosensors for digital air monitoring in crowded workplaces. Full article

Crowd counting is a critical computer vision task with significant applications in public security and smart city systems. While deep learning has markedly improved accuracy, persistent challenges include extreme scale variations, severe occlusion, and complex background clutter. To address these issues, we propose a novel Hybrid Multi-Scale Transformer-CNN U-shaped Network (HMSTUNet). Our key contributions are: a hybrid architecture integrating a Multi-Scale Vision Transformer (MSViT) for capturing long-range dependencies and a Dynamic Convolutional Attention Block (DCAB) for modeling local density patterns; and a U-shaped encoder–decoder with skip connections for effective multi-level feature fusion. Extensive evaluations on five public benchmarks show that HMSTUNet achieves the best Mean Absolute Error (MAE) on all five datasets and the best Mean Squared Error (MSE) on three. It sets new state-of-the-art records, attaining MAE/MSE of 49.1/77.8 on SHA, 6.2/10.3 on SHB, 142.1/192.7 on UCF_CC_50, 77.9/132.5 on UCF-QNRF, and 43.2/119.6 on NWPU-Crowd. These results demonstrate the model’s strong robustness and generalization capability. Full article

Smart cities increasingly reuse existing Wi-Fi infrastructure to sense crowding, but many smart-campus tools still fail to support routine, day-to-day decisions. A short-horizon field feasibility study was conducted to prototype a low-maintenance, prefix-based early-warning rule that turns anonymized campus Wi-Fi access-point counts into an interpretable lunchtime crowd signal. Daily 7-min access-point profiles from five university canteens (11:00–14:00) were aggregated, winsorized, smoothed, and row-z-scored, then clustered into demand-shape typologies using k-means++. Two typologies were obtained (Early Peak and Late Shift), and a cosine-similarity atlas was frozen. At 11:28, the five-bin occupancy prefix was compared to typology centroids, and an Early Peak badge was issued when similarity to the Early Peak centroid exceeded a preset threshold. On held-out days, the Early Peak typology could be identified at 11:28 with coverage of 0.73 and agreement of 0.86 relative to end-of-day labels. In 20 matched canteen-weekday pairs, badge days were associated with a Hodges–Lehmann median reduction of 0.193 standard-deviation units in peak crowding (≈9% lower). Given the short (3-week) horizon and limited hold-out window, results are presented as feasibility evidence and motivate a larger controlled evaluation. Simple, interpretable rules built on existing Wi-Fi telemetry were shown to be deployable as a feasibility-level decision aid on a smart campus, while broader smart-city transferability should be validated through longer-horizon controlled evaluations. Full article

Efficient monitoring of large-scale poultry farms requires the timely identification of dead broilers, as delays can accelerate disease transmission, leading to significant economic loss. Nevertheless, manual inspection remains the dominant practice, resulting in a labor-intensive, inconsistent, and poorly scalable workflow. Although recent advances in computer vision have introduced automated alternatives, most existing approaches face difficulties in crowded settings where live and dead broilers share similar visual patterns, and occlusions frequently occur. To address these problems, we propose a transformer-based multi-task segmentation framework designed to operate reliably in visually complex farm environments. The model constructs a unified feature representation that supports precise segmentation of dead broilers, while an auxiliary dead broiler counting task contributes additional supervisory features that enhance segmentation performance across diverse scene configurations. Experimental evaluations indicate that the proposed method yields accurate and stable segmentation results under various farm conditions, including densely populated and visually intricate scenes. Moreover, its overall segmentation accuracy consistently surpasses that of existing approaches, demonstrating the effectiveness of integrating transformer-based global modeling with the auxiliary regression objective. Full article

Crowd counting is a significant task in computer vision. By combining the rich texture information from RGB images with the insensitivity to illumination changes offered by thermal imaging, the applicability of models in real-world complex scenarios can be enhanced. Current research on RGB-T crowd counting primarily focuses on feature fusion strategies, multi-scale structures, and the exploration of novel network architectures such as Vision Transformer and Mamba. However, existing approaches face two key challenges: limited robustness to illumination shifts and insufficient handling of scale discrepancies. To address these challenges, this study aims to develop a robust RGB-T crowd counting framework that remains stable under illumination shifts, through introduces two key innovations beyond existing fusion and multi-scale approaches: (1) a cross-modal adaptive fusion module (CMAFM) that actively evaluates and fuses reliable cross-modal features under varying scenarios by simulating a dynamic feature selection and trust allocation mechanism; and (2) a multi-scale aggregation module (MSAM) that unifies features with different receptive fields to an intermediate scale and performs weighted fusion to enhance modeling capability for cross-modal scale variations. The proposed method achieves relative improvements of 1.57% in GAME(0) and 0.78% in RMSE on the DroneRGBT dataset compared to existing methods, and improvements of 2.48% and 1.59% on the RGBT-CC dataset, respectively. It also demonstrates higher stability and robustness under varying lighting conditions. This research provides an effective solution for building stable and reliable all-weather crowd counting systems, with significant application prospects in smart city security and management. Full article

Counting small, densely clustered objects from low-altitude aerial views is challenging due to large scale variations, complex backgrounds, and severe occlusion, which often degrade the performance of fully supervised or density-regression methods. To address these issues, we propose a weakly supervised crowd counting framework that leverages point-level supervision and a feature-adaptive fusion strategy to enhance perception under low-altitude aerial views. The network comprises a front-end feature extractor and a back-end fusion module. The front-end adopts the first 13 convolutional layers of VGG16-BN to capture multi-scale semantic features while preserving crucial spatial details. The back-end integrates a Feature-Adaptive Fusion module and a Multi-Scale Feature Aggregation module: the former dynamically adjusts fusion weights across scales to improve robustness to scale variation, and the latter aggregates multi-scale representations to better capture targets in dense, complex scenes. Point-level annotations serve as weak supervision to substantially reduce labeling cost while enabling accurate localization of small individual instances. Experiments on several public datasets, including ShanghaiTech Part A, ShanghaiTech Part B, and UCF_CC_50, demonstrate that our method surpasses existing mainstream approaches, effectively mitigating scale variation, background clutter, and occlusion, and providing an efficient and scalable weakly supervised solution for small-object counting. Full article

Small object localization is one of the most challenging tasks owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. Recent advances in localizing small objects are mainly dependent on regression-based counting approaches, which require considerable annotations for training. As a contrast, human learners can quickly master labeling skills from only a few annotation examples. In this paper, we attempt to simulate this training mechanism and propose a novel positive-unlabeled (PU) learning based approach that can localize small objects by learning from partial point annotations. We evaluate our approach on five typical datasets of small objects involving a single cell, an animal/insect, and human crowds. Quantitative experimental results show that our approach has achieved inspiring localization performance (F1 score > 0.75) even under the supervision of less than 10% of the overall point annotations. This approach paves the way for low-annotation-cost single-cell analysis within microfluidic droplets. Full article

Understanding past and present maritime activity patterns is critical for navigation safety, environmental assessment, and commercial operations. An increasing number of services now openly provide positioning data from the Automatic Identification System (AIS) via ground-based receivers. We show that coastal vessel activity can be reconstructed from open access data with high accuracy, even with limited data quality and incomplete receiver coverage. For three months of open AIS data in the Baltic Sea from August to October 2024, we present (i) cleansing and reconstruction methods to improve the data quality, and (ii) a journey model that converts AIS message data into vessel counts, traffic estimates, and spatially resolved vessel density at a resolution of ∼400 m. Vessel counts are provided, along with their uncertainties, for both moving and stationary activity. Vessel density maps also enable the identification of port locations, and we infer the most crowded and busiest coastal areas in the Baltic Sea. We find that on average, ≳4000 vessels simultaneously operate in the Baltic Sea, and more than 300 vessels enter or leave the area each day. Our results agree within 20% with previous studies relying on proprietary data. Full article

Crown width (CW) is a critical metric for characterizing tree-canopy dimensions; however, its direct measurement remains labor-intensive and is often impractical in inaccessible crowns. Consequently, CW is frequently derived from projections, which are susceptible to multiple sources of imprecision, including canopy density, crown irregularity, terrain heterogeneity, and the observer’s vantage point, especially in structurally complex natural forests. While deep neural network (DNN) models show substantial potential for CW prediction, their performance in heterogeneous forests remains uncertain. We developed DNN models integrated with a Height Threshold Method (HTM) to predict individual-tree CW in the natural mixed forests of Northern China, dominated by Larix principis-rupprechtii and Picea asperata. Our study further compared the relative importance of feature engineering versus model architectural complexity in predictive accuracy and identified the key ecological variables governing CW. The model performance was evaluated through the coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Field surveys of 34 representative sample plots produced 1884 individual-tree records. The main results were as follows: (1) all DNNs avoided overfitting, and were statistical stable under ten-fold cross-validation; (2) the optimized DNN3-2 model (tuned hidden layer count, neurons/hidden layer, L₂ regularization, and dropout) achieved peak performance, explaining 69% of CW variance with residuals with stable variance and excellent coverage properties; (3) tree size, neighborhood competition, species identity, and site quality were the most important predictors; and (4) stand parameters calculated from competitive neighborhoods defined by the HTM, particularly mean stand crowding, Simpson’s index (1-D), and Shannon’s index (H′), significantly improved prediction accuracy. By integrating DNN with the HTM, our approach allows for accurate prediction of individual-tree CW in natural mixed forests of Northern China, dominated by Larix principis-rupprechtii and Picea asperata. Full article

With rapid urbanization and growing population inflows into metropolitan areas, crowd gatherings have become increasingly frequent and dense, posing significant challenges to public safety management. Although existing crowd gathering detection methods have achieved notable progress, they still face major limitations: most rely heavily on local texture or density features and lack the capacity to model contextual information, making them ineffective under severe occlusions and complex backgrounds. Additionally, fixed-scale feature extraction strategies struggle to adapt to crowd regions with varying densities and scales, and insufficient attention to densely populated areas hinders the capture of critical local features. To overcome these challenges, we propose a point-supervised framework named Multi-Scale Convolutional Attention Network (MSCANet). MSCANet adopts a context-aware architecture and integrates multi-scale feature extraction modules and convolutional attention mechanisms, enabling it to dynamically adapt to varying crowd densities while focusing on key regions. This enhances feature representation in complex scenes and improves detection performance. Extensive experiments on public datasets demonstrate that MSCANet achieves high counting accuracy and robustness, particularly in dense and occluded environments, showing strong potential for real-world deployment. Full article

The COVID-19 pandemic highlighted the need for a scientific framework that enables quantitative assessment and control of airborne infection risks in indoor environments. This study identifies limitations in the traditional Wells–Riley model—specifically its assumptions of perfect mixing and steady-state conditions—and addresses these shortcomings by adopting the REHVA (Federation of European Heating, Ventilation and Air Conditioning Associations) infection risk assessment model. We propose a five-tier risk classification system (Monitor, Caution, Alert, High Risk, Critical) based on two key metrics: the probability of infection (Pₙ) and the event reproduction number (R_event). Unlike the classical model, our approach integrates airborne virus removal mechanisms—such as natural decay, gravitational settling, and filtration—with occupant dynamics to reflect realistic contagion scenarios. Simulations were conducted across 10 representative indoor settings—such as classrooms, hospital waiting rooms, public transit, and restaurants—considering ventilation rates and activity-specific viral emission patterns. The results quantify how environmental variables (ventilation, occupancy, time) impact each setting’s infection risk level. Our findings indicate that static mitigation measures such as mask-wearing or physical distancing are insufficient without dynamic, model-based risk evaluation. We emphasize the importance of incorporating real-time crowd density, occupancy duration, and movement trajectories into risk scoring. To support this, we propose integrating computer vision (CCTV-based crowd detection) and entry/exit counting sensors within a live airborne risk assessment framework. This integrated system would enable proactive, science-driven epidemic control strategies, supporting real-time adaptive interventions in indoor spaces. The proposed platform could serve as a practical tool for early warning and management during future airborne disease outbreaks. Full article