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

arXiv: https://arxiv.org/abs/2602.00153 · HTML: https://mlsanity.com/paper/2602.00153/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 8 of 8)

- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Fast Object Detection in Compressed Video](https://mlsanity.com/paper/1811.11057/) (2018)
- [MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection](https://mlsanity.com/paper/1805.00107/) (2018)
- [Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation](https://mlsanity.com/paper/2103.14529/) (2021)
- [Object Detection in the DCT Domain: is Luminance the Solution?](https://mlsanity.com/paper/2006.05732/) (2020)

## Built on by (most cited first, 4 of 4)

- [Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream](https://mlsanity.com/paper/2607.19476/) (2026)
- [MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams](https://mlsanity.com/paper/2608.10790/) (2026)
- [Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge](https://mlsanity.com/paper/2609.29648/) (2026)
- [Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection](https://mlsanity.com/paper/2609.34142/) (2026)

## Abstract (arXiv)

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.