K-track: Kalman-Enhanced Tracking Achieves Acceleration of Deep Point Trackers on Edge Devices
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Point tracking, a crucial element in fields like robotics and augmented reality, currently faces significant challenges when implemented on devices with limited processing power. Bishoy Galoa, Pau Closas, and Sarah Ostadabbas from Northeastern University address this problem with K-Track, a new framework that accelerates deep learning-based point trackers for use on edge devices. K-Track cleverly combines the strengths of sparse deep learning with Kalman filtering, allowing it to predict movement between keyframes and dramatically reduce computational demands. The result is a system that achieves five to ten times faster performance while maintaining over 85% of the accuracy of existing trackers, offering a practical solution for deploying advanced point tracking in real-world applications and bridging the gap between research and deployable vision systems.
Kalman Filtering Accelerates Point Tracking on Edges This document summarizes the research behind K-Track, a novel framework designed to accelerate point tracking on resource-constrained edge devices like the NVIDIA Jetson Nano, without significant accuracy loss. Modern point tracking algorithms, such as CoTracker3 and SpatialTracker, achieve high accuracy but demand substantial computational resources, hindering real-time performance. K-Track addresses this limitation by combining sparse deep learning inference with a Kalman filter. Instead of processing every frame with a deep learning model, K-Track leverages the inherent smoothness of trajectories, applying the model sparsely, every N frames, and using the Kalman filter to predict and smooth the trajectory between these keyframes. Importantly, K-Track works with any existing point tracking algorithm, requiring no modifications, making it a plug-and-play acceleration framework. The system achieves a 5-10x speedup while maintaining over 85% accuracy compared to full-frame inference. Future work focuses on integrating cheaper, more frequent measurements, like optical flow, with the sparse deep learning data to further improve accuracy and efficiency, and dynamically adjusting the inference frequency based on scene dynamics and tracking confidence. In essence, K-Track is a smart acceleration technique that reduces computational load without sacrificing tracking performance, making advanced point tracking practical for a wider range of applications on edge devices.
Sparse Deep Learning and Kalman Filtering The research team developed K-Track, a novel acceleration framework designed to deploy high-performance point tracking on resource-constrained edge devices, such as the Jetson Nano and RTX Titan. This system addresses the significant computational demands of modern deep learning-based trackers by intelligently combining sparse deep learning inference with lightweight Kalman filtering. K-Track operates in two distinct phases, enabling substantial speedups while preserving tracking accuracy. During a Warmup Phase, the deep learning tracker processes consecutive frames to establish initial motion dynamics and initialize the Kalman Filter.
The Hybrid Tracking Phase then alternates between running the deep learning tracker at keyframes, occurring every N frames, and relying solely on efficient Kalman Filter predictions for intermediate frames. At each keyframe, the deep learning tracker provides updated measurements to refine the Kalman Filter, ensuring continued accuracy and temporal coherence. This architecture achieves an approximate N× speedup by drastically reducing the frequency of computationally expensive deep learning inference, while the Kalman Filter maintains tracking continuity during intermediate frames. Researchers evaluated K-Track across three state-of-the-art point trackers, CoTracker3, SpatialTracker, and Track-On, demonstrating its tracker-agnostic compatibility and plug-and-play functionality. Experiments meticulously quantified the performance gains, achieving a 5-10x speedup while retaining over 85% of the original trackers’ accuracy.
Sparse Keyframes Accelerate Video Point Tracking Scientists have developed K-Track, a new framework that significantly accelerates point tracking in video sequences while preserving accuracy, enabling deployment on resource-constrained edge devices. The work addresses a critical limitation of modern deep learning-based trackers, which demand substantial computational resources, hindering their use in applications like robotics and augmented reality where immediate visual feedback is essential. Experiments demonstrate that K-Track achieves a speedup of 5 to 10times compared to running deep learning trackers alone. The breakthrough combines sparse deep learning keyframe updates with lightweight Kalman filtering to predict movement between frames. This hybrid strategy maintains over 85% of the original trackers’ accuracy while dramatically reducing computational load. Specifically, the system alternates between running the deep learning tracker on keyframes, occurring every N frames, and relying solely on Kalman filter predictions for intermediate frames. This architecture achieves an N-fold speedup by minimizing the frequency of computationally expensive deep learning inference. Researchers evaluated K-Track across multiple state-of-the-art point trackers, including CoTracker3, SpatialTracker, and Track-On, demonstrating real-time performance on platforms such as the Jetson Nano and RTX Titan.
The team confirmed that K-Track is a tracker-agnostic framework, functioning as a plug-and-play acceleration system that requires no modification to the underlying tracker architecture or retraining.
Fast Accurate Point Tracking with K-Track Scientists have developed K-Track, a new framework that significantly accelerates point tracking in video sequences while maintaining high accuracy. This achievement addresses a critical limitation of current deep learning-based trackers, which demand substantial computational resources and are difficult to deploy on edge devices with limited power and processing capabilities. K-Track combines sparse deep learning measurements with Kalman filtering, enabling a five to ten-fold increase in speed without sacrificing over 85% of tracking accuracy. The researchers demonstrate that K-Track effectively bridges the gap between advanced tracking algorithms and real-world applications by enabling real-time performance on platforms such as the NVIDIA Jetson Orin Nano. Extensive evaluation using established benchmarks confirms the framework’s ability to maintain tracking consistency even with challenging motion and camera instability.
The team acknowledges that the Kalman filter’s performance relies on accurate initial measurements, and future work will explore incorporating multi-rate sensor fusion, combining high-accuracy deep learning data with more frequent, albeit noisier, measurements from alternative tracking methods to potentially further enhance both accuracy and efficiency. 👉 More information 🗞 K-Track: Kalman-Enhanced Tracking for Accelerating Deep Point Trackers on Edge Devices 🧠 ArXiv: https://arxiv.org/abs/2512.10628 Tags: