Search Results (246)

Although urban streetscape visual esthetic quality (VAQ) assessment has progressed markedly, its findings are rarely operationalized in urban planning policy-making. The resulting discontinuity in the assessment–policy linkage is a critical impediment to streetscape VAQ enhancement. We propose an eye-tracking-enabled, end-to-end decision support framework that links evidence acquisition, intervention prioritization, design strategy formulation, and outcome feedback. Eye tracking is integrated to establish a three-dimensional assessment system spanning spatial, psychological, and physiological dimensions. Within this integrated system, we construct a three-level eye-tracking-based visual characteristics (ET-VC) framework across streetscape elements, formal characteristics, and public esthetic perception (PAP). Together, the three-dimensional system provides a theoretical basis for acquiring the multi-modal data required for VAQ enhancement. Building on this integrated assessment, we embed scenario planning theory to construct a planning facing decision model with PAP as the core outcome. The model combines importance-performance analysis (IPA) with the coupling coordination degree model (CCDM) to guide resource allocation decisions and intervention prioritization, and further uses eye-tracking evidence to support the development of refined, actionable enhancement strategies. A case study in Wudadao validates the framework’s robustness and feasibility. The ET-VC results provide additional evidence for interpreting esthetic perception: (1) ET-VC indicators differ significantly across streetscape elements, and “being viewed more” does not necessarily correspond to higher esthetic ratings; (2) four groups of key formal characteristic indicators—color configuration, naturalness, historicity and planning/regulatory control, and visual scale—systematically reshape fixation onset and maintenance patterns; and (3) PAP appears to involve partially nonlinear relationships between material landscape features and additional top-down influences (e.g., historical narratives and individual experience), rather than being fully explained by linear associations alone. Overall, this study provides both a theoretical basis and an applied demonstration for evidence-based streetscape VAQ enhancement. Full article

Near-miss events, defined as hazardous traffic interactions without actual collisions, provide valuable indicators for proactive traffic safety assessment. However, existing studies mainly focus on collision detection or object-level perception, while near-miss interactions and their severity remain insufficiently explored. This study proposes a video-based framework for real-time near-miss detection and risk evaluation in complex urban intersections. The framework integrates an enhanced YOLOv11 detector with a small-object detection head, BoT-SORT multi-object tracking, and bird’s-eye-view (BEV) transformation to accurately extract trajectories and motion features of heterogeneous road users. A Near-Miss Risk Index (RI) is developed by jointly considering spatial proximity, time-to-collision, and motion intensity to quantify near-miss severity levels. Experimental results on real-world CCTV data demonstrate that the proposed method effectively identifies high-risk interactions among vehicles, motorcycles, and pedestrians, providing interpretable severity assessment and supporting proactive traffic safety analysis for intelligent transportation systems. Full article

Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. Full article

The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures. Full article

Propagating easterly waves (EW) are analyzed here, within the dynamical environment of the Caribbean Low-Level Jet (CLLJ) using radiosondes from the Organization of Tropical East Pacific Convection (OTREC) field campaign, ERA5 reanalysis, and lightning from the World Wide Lightning Location Network (WWLLN) over $5^{\circ }$–${20}^{\circ }$ N, ${60}^{\circ }$–${100}^{\circ }$ W during 21 August–30 September 2019. Radiosondes resolve the vertical structure of the waves at San Andrés (Colombia), Limón and Santa Cruz–Guanacaste (Costa Rica), while ERA5 provides spatial–temporal continuity and vertically integrated diagnostics—namely, the vertically integrated moisture flux divergence (VIMFD) and the vertically integrated geopotential flux divergence (VIGFD). Lightning from WWLLN and precipitation from ERA5 and the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement mission (GPM IMERG) offer independent convective proxies to track disturbances. Mean profiles from radiosondes and ERA5 show strong agreement at Limón and Guanacaste and some differences at San Andrés, yet all datasets capture coherent, phase-locked anomalies in zonal wind, meridional wind, temperature, humidity, vertical velocity and vorticity used to diagnose EW–CLLJ interactions. VIMFD, VIGFD, lightning and precipitation exhibit westward-propagating cores that align with the above anomalies, indicating that organized convection is coupled to the disturbances, whereas the mean state preconditions the environment to enable wave-induced upward motion. A robust vertical adjustment of the CLLJ is documented: the core shifts from near 925 hPa over the Caribbean Sea to about 700 hPa over the Eastern Tropical Pacific ($\Delta p\sim 150$ hPa). This feature is reproduced by a 30-year ERA5 climatology, consistent with jet-exit forcing and enhanced boundary-layer coupling over land. Conditions favorable for barotropic instability using the Rayleigh–Kuo criterion, were present over most of the period. A qualitative barotropic conversion proxy, computed from the eddy momentum covariance $⟨u^{\prime }{v}^{\prime }⟩$, shows positive values in the lower troposphere at Guanacaste and in the layer 850–700 hPa at San Andrés, suggesting mean-to-eddy momentum transfer, whereas the signal at Limón is weaker. Together, these results provide a physically consistent view of EW–CLLJ interactions across the IAS; therefore, a schematic of those mechanisms is proposed here. The results highlight the need for high-resolution modeling and full energy-budget analyses. Full article

Space-based multi-target passive tracking is critical for space situational awareness, but faces severe challenges due to the limited field-of-view (FoV) and directional ambiguity of onboard sensors. These constraints often lead to target loss, poor observability, and decreased estimation accuracy. To address these issues, different fusion architectures have been explored. While centralized measurement-level fusion offers superior accuracy for estimating target states, distributed estimation-level fusion provides greater reliability for estimating the number of targets. To adaptively leverage these two complementary strengths, a dynamic hierarchical fusion method through real-time optimization of the fusion topology is proposed. Specifically, at each decision epoch, sensor nodes are dynamically partitioned into local fusion nodes (LFNs) and detection-only nodes (DONs). Each LFN receives measurements from selected DONs and executes an iterated-correction Gaussian-mixture probability hypothesis density filter. Subsequently, LFNs share and fuse their estimates using the intensity-dependent arithmetic average fusion. This dynamic process is achieved by applying a sensor management scheme based on partially observable Markov decision process (POMDP). To ensure accurate cardinality estimation, the reward function in POMDP utilizes the posterior expected number of targets. The resultant optimization is efficiently solved using a binary particle swarm optimization algorithm. Numerical and hardware-in-the-loop simulations demonstrate the effectiveness of the proposed method in balancing the accuracy of target number and state estimation. Full article

In visible-light optical observations for Space Surveillance and Tracking (SST) from ground-based dynamic platforms, attitude disturbances and field-of-view discontinuities frequently undermine interframe geometric consistency, leading to energy diffusion and unstable gain in multi-frame stacking for faint space objects. We propose orbit-prior- guided target-centered stacking (OPG-TCS), a tracking-oriented post-processing method designed to stabilize target energy accumulation and improve enhancement reliability under dynamic observation conditions. OPG-TCS performs frame-wise astrometric calibration using star fields (WCS) and leverages projected orbit priors to predict target pixel locations, enabling local cropping and target-centered alignment/stacking without relying on full-frame geometric consistency. We evaluate OPG-TCS on multiple real-world dynamic-platform sequences and compare it with direct stacking and representative robust baselines. Signal-to-noise ratio (SNR) is used as the primary metric, while auxiliary indicators of peak prominence, energy concentration, and shape consistency are employed to assess robustness across varying stacking depths. The results show that OPG-TCS provides stable enhancement over different frame counts; in representative 50-frame fusions, its relative SNR surpasses direct stacking by 33.7–97.8%. These findings suggest that OPG-TCS offers a practical and robust enhancement strategy for SST-oriented observation of faint space objects, supporting more reliable detection and subsequent tracking analysis. Full article

Three-dimensional (3D) glacier velocities capture the full dynamic behavior of ice masses. For marine-terminating glaciers, acquiring 3D velocity fields is particularly critical for quantifying ice discharge into the ocean, assessing the stability of floating ice tongues, and constraining ice–ocean interactions that govern submarine melting, calving processes, and freshwater fluxes to the ocean. To further investigate glacier dynamics and elucidate ice–ocean interaction mechanisms, this study analyzed the 3D velocity of the Petermann Glacier throughout 2021 using long-term Sentinel-1 synthetic aperture radar (SAR) observations. First, two-dimensional velocity time series were derived from ascending and descending SAR images, and the glacier’s 3D velocity components were reconstructed based on the geometric relationships between the two viewing geometries. The estimated 3D velocities were then used as prior constraints, and glacier motion was treated as a continuously evolving state variable within a Kalman filtering framework. Multi-track, asynchronous remote sensing observations were integrated into a unified system to obtain a stable and temporally continuous 3D velocity field. Finally, statistical analyses of the 3D velocity time series were conducted to characterize spatiotemporal variations, seasonal patterns, and topographic influences on glacier motion, thereby providing quantitative insights into the dynamic coupling between glacier and ocean. Full article

As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlaying information within the forward field of view, yet empirical evidence remains limited on how specific HUD presentation strategies reshape older drivers’ visual attention allocation. Grounded in theories of visual attention and cognitive load, this study systematically investigates three design variables that are increasingly common in contemporary HUDs (including AR-HUDs): (1) dynamic versus static navigation cues, (2) pedestrian warning strategies under different lighting conditions, and (3) the spatial placement of high-priority information. We first conducted a formative user study to define variables and operationalizations, and then carried out three within-subject driving-simulator experiments using controlled HUD stimuli and eye tracking. Objective gaze measures (e.g., fixation count, total fixation duration, and time to first fixation) were combined with subjective preference ratings to characterize attentional capture, search efficiency, and potential attentional costs. Findings reveal a robust trade-off: continuously changing navigation cues enhance attentional capture but can also increase attentional “stickiness,” unnecessarily consuming older drivers’ limited attentional resources. In pedestrian hazard tasks, real-time overlay warnings that were spatially aligned with the hazard significantly improved visual localization under low-light conditions, outperforming early warnings and multi-stage strategies. Across tasks and layout conditions, the central HUD region showed a stable attentional advantage—placing critical information centrally elicited greater visual attention and stronger subjective preference. These results provide mechanistic evidence for how HUD parameters modulate older drivers’ attention and yield actionable implications for prioritization, temporal pacing of dynamic navigation cues, and a “center-first” layout strategy to guide age-friendly HUD design. Full article

Accurate environmental perception is fundamental to safe autonomous driving; however, most existing multimodal systems rely on fixed or heuristic sensor fusion strategies that cannot adapt to scene-dependent variations in sensor reliability. This paper proposes Cross-Modal Adaptive Attention (CMAA), a unified end-to-end Bird’s-Eye-View (BEV) perception framework that dynamically fuses camera, LiDAR, and RADAR information through learnable, context-aware modality gating. Unlike static fusion approaches, CMAA adaptively reweights sensor contributions based on global scene descriptors, enabling the robust integration of semantic, geometric, and motion cues without manual tuning. The proposed architecture jointly performs 3D object detection, multi-object tracking, and motion forecasting within a shared BEV representation, preserving spatial alignment across tasks and supporting efficient real-time deployment. Experiments conducted on the official nuScenes validation split demonstrate that CMAA achieves 0.528 mAP and 0.691 NDS, outperforming fixed-weight fusion baselines while maintaining a compact model size and efficient inference. Additional tracking evaluation using the official nuScenes tracking devkit reports improved tracking performance, while motion forecasting experiments show reduced trajectory displacement errors (minADE and minFDE). Ablation studies further confirm the complementary contributions of adaptive modality gating and bidirectional cross-modal refinement, and a stratified dynamic analysis reveals consistent reductions in velocity estimation error across object classes, motion regimes, and environmental conditions. These results demonstrate that adaptive multimodal fusion improves robustness, motion reasoning, and perception reliability in complex traffic environments while remaining computationally efficient for deployment in safety-critical autonomous driving systems. Full article

This work introduces an integrated Material Point Method (MPM) framework for optimizing tendon-driven hyperelastic robots under extreme 3D deformations. To overcome the mesh distortion limitations of the traditional FEM at large strains, we develop a coupled MPM–tendon hyperelastic model that integrates Yeoh constitutive laws with discrete tendon actuation forces. The model enables robust simulation of anisotropic stress propagation through Lagrangian particle tracking and Eulerian grid discretization, eliminating mesh entanglement artifacts. A strain-gradient-driven tendon path algorithm ensures mechanical efficiency using Fréchet distance-based similarity metrics and curvature smoothness screenin, enforcing spatial continuity in complex topologies. Validation demonstrates: (1) Sub 3 mm geometric errors and about 89% volumetric overlap in worm-inspired deformations; (2) optimal computational efficiency at 0.4–0.6 mm grid densities, balancing accuracy and resource overhead; and (3) projected alignment errors of 0.8 mm (XY), 1.3 mm (XZ), and 2.9 mm (YZ) in multi-view spatial analyses. The framework achieves about 89% ± 2% volumetric overlap in quadrupedal morphing via agonist–antagonist tendon optimization, demonstrating efficacy for extreme 3D deformation control. Full article

This study addresses key challenges in obtaining reliable infrared data for maritime ship observation and limitations of existing models, such as simplified reflectance assumptions and incomplete multi-band coverage. To improve modeling accuracy and computational efficiency, a high-precision Bidirectional Reflectance and Pseudo-random Vector Enhanced Reverse Monte Carlo Method (BP-ERMCM) is developed. By combining the Bidirectional Reflectance Distribution Function (BRDF), pseudo-random vector approaches, and improved ray-tracking algorithms with precomputed thermal radiation and MODTRAN’s atmospheric transfer model, BP-ERMCM provides multi-view infrared characteristic simulations across 3–5 μm and 8–12 μm bands. Simulations using a 3D ship model with 191 viewpoints reveal seasonal sensitivity, with summer peak intensity at 9.8 μm being 39.3% higher than in winter, and viewpoint dependency showing oblique overhead radiation 5.65 times greater than that from bow angles. Long-wave contours enhance target distinction, while mid-wave regions are dominated by reflection, increasing intensity at 3.8 μm by 56.1–85.7%. These findings highlight BP-ERMCM’s potential to inform infrared signature database construction, detector optimization, and maritime observation strategies. The findings underscore BP-ERMCM’s capability to enhance efficiency and accuracy, providing valuable insights for infrared databases, sensor selection, and maritime observation strategies, thereby advancing infrared signature analysis in maritime applications. Full article

Powder-bed arc additive manufacturing (PBAAM) may reduce the cost of powder-bed metal additive manufacturing and enable thicker layers than laser powder bed fusion (LPBF), but melt-track stability limits are not well established. Here, 316L stainless steel powder (15–53 µm) was melted by a TIG-based arc in a custom powder-bed system while varying current, travel speed, layer thickness and hatch distance. Single tracks on an inclined bed (≈0–0.4 mm thickness) were used to identify continuity loss and melt-pool width, quantified from top-view images via width profiles, a gap-based continuity metric and the coefficient of variation. Parallel-track tests at 0.15, 0.20 and 0.25 mm layer thickness with hatch distances set to 25%, 50% and 75% of the measured melt-pool width assessed inter-track bonding and lack of fusion, and selected parameters were validated in five-layer builds. Higher current with low-to-moderate travel speeds produced wider, more stable melt pools on the inclined bed. Hatch ratios of 25–50% were the most effective for sustaining fusion in single layers and multi-layer builds, whereas 75% promoted unbonded regions and narrow-track morphologies. Overall, PBAAM can process substantially thicker layers with relatively simple equipment, but requires a narrow, carefully tuned window to balance continuity, fusion and heat accumulation. Full article

Automated verification of preflight aircraft inspection—a critical safety procedure—is addressed by integrating multi-camera tracking with procedural knowledge through a symmetry-aware spatio-temporal graph model. Departing from conventional tracking paradigms, the framework encodes operational protocols and structural symmetries of the aircraft as explicit constraints for trajectory association. Semantically consistent inspection zones are derived from geometric symmetry, and reliable tracklets extracted within them are connected using rules that enforce temporal order and identity consistency. Verification is formulated as a constrained shortest-path search over this graph, ensuring sequential and complete coverage of all mandatory zones by a single inspector. Evaluated on real-world airport surveillance data across diverse conditions, the proposed approach achieves a Complete Inspection Success Rate of 86.5%, significantly outperforming state-of-the-art tracking and re-identification baselines. The results demonstrate that explicit procedural integration substantially enhances the reliability and interpretability of automated compliance verification in safety-critical industrial monitoring. Full article

Tracking worker identities across cameras is increasingly important for advanced construction site monitoring, such as safety and productivity monitoring. However, current computer vision-based tracking faces challenges in reliably associating worker identities due to frequent occlusions and extreme viewpoint shifts between aerial and ground cameras, resulting in fragmented trajectories and ID switches. This study proposes a Dual-Stage Graph-based Association framework that integrates worker detections across multiple views using complementary Re-identification models and camera-aware adaptive thresholding. The framework synergistically combines TransReID for viewpoint-invariant global features and BPBReID for occlusion-robust part-based features, producing more discriminative representations. Data association leverages a graph-based clustering approach to combine representation features, camera topology, and temporal cues for robust identity maintenance. The first stage enables cross-view clustering while preventing false matches, and the second stage ensures long-term identity stability through EMA-based gallery management. Experiments on two construction sites demonstrate that the proposed framework achieves an HOTA of 39.85% and an IDF1 of 63.58%, outperforming existing baselines while reducing ID switches by 35.0%. Results on the AG-ReID.v2 benchmark demonstrate strong generalization with 90.82% Rank-1 accuracy in aerial-to-CCTV matching. The approach highlights initial feasibility for cross-view multi-camera tracking in construction with potential for extension to more complex industrial environments. Full article