See Without Decoding: Motion-Vector-Based Tracking in Compressed Video

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Authors: Axel Duché, Clément Chatelain, Gilles Gasso
Cited by: 0

Abstract

We propose a lightweight compressed-domain tracking model that operates directly on video streams, without requiring full RGB video decoding. Using motion vectors and transform coefficients from compressed data, our deep model propagates object bounding boxes across frames, achieving a computational speed-up of order up to 3.7 with only a slight 4% mAP@0.5 drop vs RGB baseline on MOTS15/17/20 datasets. These results highlight codec-domain motion modeling efficiency for real-time analytics in large monitoring systems.

Fields of Study & Subjects

Video Surveillance and Tracking Methods Advanced Neural Network Applications Video Coding and Compression Technologies

Publication details

Journal / source
arXiv (Cornell University)
Publication date
29 January 2026
Document type
Preprint