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Computer Science > Computer Vision and Pattern Recognition

arXiv:2608.10790 (cs)
[Submitted on 11 Aug 2026]

Title:MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams

Authors:Iñaki Erregue, Kamal Nasrollahi, Sergio Escalera
View a PDF of the paper titled MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams, by I\~naki Erregue and 2 other authors
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Abstract:Deploying modern video trackers at scale is bottlenecked by the computational cost of RGB-based object detectors. To this end, we present MVTrack, an ultrafast tracker for moving objects that operates directly on H.264 bitstreams. MVTrack combines MVDet, a lightweight detector for motion vector fields, with MVLink, a minimalist kinematic association module. On VIRAT, MVTrack outperforms YOLO26n while using 60$\times$ fewer parameters, requiring 40$\times$ fewer FLOPs, and reducing CPU latency by 8.6$\times$. These results demonstrate that compressed video data alone can enable accurate and scalable surveillance tracking, thereby bypassing the need for pixel reconstruction.
Comments: This paper has been accepted to the 2nd workshop on Low-Level Vision Frontiers with Generative AI, Preference Optimization, Agentic Systems and World Models (LoViF) at ECCV2026
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.10790 [cs.CV]
  (or arXiv:2608.10790v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2608.10790
arXiv-issued DOI via DataCite

Submission history

From: Iñaki Erregue [view email]
[v1] Tue, 11 Aug 2026 10:53:22 UTC (8,115 KB)
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