arXiv:2512.10628v1 Announce Type: new 
Abstract: Point tracking in video sequences is a foundational capability for real-world computer vision applications, including robotics, autonomous systems, augmented reality, and video analysis. While recent deep learning-based trackers achieve state-of-the-art accuracy on challenging benchmarks, their reliance on per-frame GPU inference poses a major barrier to deployment on resource-constrained edge devices, where compute, power, and connectivity are limited. We introduce K-Track (Kalman-enhanced Tracking), a general-purpose, tracker-agnostic acceleration framework designed to bridge this deployment gap. K-Track reduces inference cost by combining sparse deep learning keyframe updates with lightweight Kalman filtering for intermediate frame prediction, using principled Bayesian uncertainty propagation to maintain temporal coherence. This hybrid strategy enables 5-10X speedup while retaining over 85% of the original trackers' accuracy. We evaluate K-Track across multiple state-of-the-art point trackers and demonstrate real-time performance on edge platforms such as the NVIDIA Jetson Nano and RTX Titan. By preserving accuracy while dramatically lowering computational requirements, K-Track provides a practical path toward deploying high-quality point tracking in real-world, resource-limited settings, closing the gap between modern tracking algorithms and deployable vision systems.

تم تقديم K-Track، إطار عمل جديد لتسريع متتبعي النقاط العميقة على الأجهزة الطرفية، والذي يعالج تحديات نشر المتتبعين المعتمدين على التعلم العميق في بيئات ذات موارد محدودة. من خلال دمج تحديثات الإطارات الرئيسية للتعلم العميق المتناثرة مع تصفية كالمان الخفيفة، يحقق K-Track تسريعًا يتراوح بين 5 إلى 10 مرات مع الحفاظ على أكثر من 85% من دقة المتتبعين الأصليين.

Se ha presentado K-Track, un nuevo marco para acelerar los rastreadores de puntos profundos en dispositivos de borde, que aborda los desafíos de implementar rastreadores basados en aprendizaje profundo en entornos con recursos limitados. Al combinar actualizaciones de keyframes de aprendizaje profundo dispersas con un filtrado de Kalman ligero, K-Track logra una aceleración de 5 a 10 veces mientras mantiene más del 85 % de la precisión de los rastreadores originales.

K-Track, un nouveau cadre pour accélérer les suiveurs de points profonds sur des appareils en périphérie, a été introduit pour relever les défis de déploiement des suiveurs basés sur l'apprentissage profond dans des environnements à ressources limitées. En combinant des mises à jour de keyframes d'apprentissage profond éparses avec un filtrage de Kalman léger, K-Track atteint une accélération de 5 à 10 fois tout en maintenant plus de 85 % de précision des suiveurs d'origine.

K-Track, a new framework for accelerating deep point trackers on edge devices, has been introduced to address the challenges of deploying deep learning-based trackers in resource-constrained environments. By combining sparse deep learning keyframe updates with lightweight Kalman filtering, K-Track achieves a 5-10X speedup while maintaining over 85% accuracy of original trackers.

K-Track: Kalman-Enhanced Tracking for Accelerating Deep Point Trackers on Edge Devices

arXiv:2601.07845v1 Announce Type: new 
Abstract: Rapid motorization in emerging economies such as India has created severe enforcement asymmetries, with over 11 million recorded violations in 2023 against a human policing density of roughly one officer per 4000 vehicles. Traditional surveillance and manual ticketing cannot scale to this magnitude, motivating the need for an autonomous, cooperative, and energy efficient edge AI perception infrastructure. This paper presents a real time roadside perception node for multi class traffic violation analytics and safety event dissemination within a connected and intelligent vehicle ecosystem. The node integrates YOLOv8 Nano for high accuracy multi object detection, DeepSORT for temporally consistent vehicle tracking, and a rule guided OCR post processing engine capable of recognizing degraded or multilingual license plates compliant with MoRTH AIS 159 and ISO 7591 visual contrast standards. Deployed on an NVIDIA Jetson Nano with a 128 core Maxwell GPU and optimized via TensorRT FP16 quantization, the system sustains 28 to 30 frames per second inference at 9.6 W, achieving 97.7 percent violation detection accuracy and 84.9 percent OCR precision across five violation classes, namely signal jumping, zebra crossing breach, wrong way driving, illegal U turn, and speeding, without manual region of interest calibration. Comparative benchmarking against YOLOv4 Tiny, PP YOLOE S, and Nano DetPlus demonstrates a 10.7 percent mean average precision gain and a 1.4 times accuracy per watt improvement. Beyond enforcement, the node publishes standardized safety events of CAM and DENM type to connected vehicles and intelligent transportation system backends via V2X protocols, demonstrating that roadside edge AI analytics can augment cooperative perception and proactive road safety management within the IEEE Intelligent Vehicles ecosystem.

تقدم دراسة جديدة عقدة إدراك Edge-AI مصممة لتحليل انتهاكات المرور في الوقت الحقيقي ونشر أحداث السلامة في الهند، مما يعالج التحديات التي تطرحها زيادة عدد المركبات وفجوة كبيرة في تطبيق القانون، مع تسجيل أكثر من 11 مليون انتهاك في عام 2023.

Un nuevo estudio presenta un nodo de percepción Edge-AI diseñado para el análisis en tiempo real de violaciones de tráfico y la difusión de eventos de seguridad en India, abordando los desafíos planteados por la rápida motorización y una significativa brecha en la aplicación de la ley, con más de 11 millones de violaciones registradas en 2023.

Une nouvelle étude présente un nœud de perception Edge-AI conçu pour l'analyse en temps réel des violations de la circulation et la diffusion d'événements de sécurité en Inde, répondant aux défis posés par la motorisation rapide et un écart d'application significatif, avec plus de 11 millions de violations enregistrées en 2023.

A new study presents an Edge-AI perception node designed for real-time traffic violation analytics and safety event dissemination in India, addressing the challenges posed by rapid motorization and a significant enforcement gap, with over 11 million violations recorded in 2023.

Edge-AI Perception Node for Cooperative Road-Safety Enforcement and Connected-Vehicle Integration

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K-Track: Kalman-Enhanced Tracking for Accelerating Deep Point Trackers on Edge Devices

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