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

TL;DR

K-Track is a hybrid tracking framework that combines sparse deep learning updates with Kalman filtering to significantly accelerate point tracking on edge devices while maintaining high accuracy.

Contribution

It introduces a general-purpose acceleration method that reduces inference costs by integrating Bayesian Kalman filtering with deep trackers, enabling real-time edge deployment.

Findings

Achieves 5-10X speedup over state-of-the-art trackers.

Maintains over 85% of original accuracy.

Demonstrates real-time performance on NVIDIA Jetson Nano and RTX Titan.

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.