arXiv Computer Vision
ALINA: Advanced Line Identification and Notation Algorithm
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arXiv Computer Vision
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Adaptive Dual-Constrained Line Aggregation for Cross-Paradigm Line Segment Detection
The paper introduces Adaptive Dual-Constrained Line Aggregation (ADLA), a framework that extracts line segments across multiple detection paradigms by aggregating pixels from an edge strength map under orientation coherence and bounded orthogonal distance constraints. ADLA dynamically updates line centroids and orientations, incorporates edge strength into various stages, and reduces parameter tuning. Experiments on generic, wireframe, and Manhattan line segment datasets show strong performance, achieving F^H scores of 0.8665, 0.8720, and 0.7297 respectively.
arXiv AI
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Situation Awareness for Intelligent Data Distribution in Connected Vehicles
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arXiv Computer Vision
Oct 21
Deep Learning-Based Road Lane Quality Assessment from Egocentric Dashcam Video
The paper introduces a geo‑referenced smartphone dashcam dataset of 63,839 frames from South Texas and a baseline framework for assessing road lane quality. Three new metrics—Contrast Stability Index (CSI), Line Continuity Ratio (LCR), and Regression Lane Confidence Score (RLCS)—are proposed to evaluate lane‑marking visibility, continuity, and map consistency without dense human labels. Baseline models achieve strong performance, and pilot validation shows the metrics correlate better with human judgments than generic image‑quality measures.
arXiv Machine Learning
Sep 10
Infrastructure-based Monocular 3D Vehicle Localization Framework with Experimental Validation
arXiv:2609.05523v1 Announce Type: cross Abstract: This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate...
arXiv Computer Vision
Oct 12
Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion
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arXiv Machine Learning
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Enhancing Road Safety Through Multi-Camera Image Segmentation with Post-Encroachment Time Analysis
The paper introduces a multi‑camera computer vision system that computes Post‑Encroachment Time (PET) in real time to assess intersection safety. Using four synchronized cameras and YOLOv11 segmentation on NVIDIA Jetson AGX Xavier devices, vehicle detections are mapped to a unified bird’s‑eye view and processed with a pixel‑level PET algorithm to generate high‑resolution heatmaps. The system records PET data in an SQL database and demonstrates real‑time throughput (2.68 FPS) with 800 × 800 heatmaps, validating a scalable, decentralized approach for high‑resolution intersection safety evaluation.