# MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection

arXiv: https://arxiv.org/abs/1805.00107 · HTML: https://mlsanity.com/paper/1805.00107/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Real-time Online Multi-Object Tracking in Compressed Domain](https://mlsanity.com/paper/2204.02081/) (2022)
- [Does Video Compression Impact Tracking Accuracy?](https://mlsanity.com/paper/2202.00892/) (2022)
- [See Without Decoding: Motion-Vector-Based Tracking in Compressed Video](https://mlsanity.com/paper/2602.00153/) (2026)
- [SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences](https://mlsanity.com/paper/2112.14934/) (2021)
- [Unveiling Document Structures with YOLOv5 Layout Detection](https://mlsanity.com/paper/2309.17033/) (2023)
- [MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams](https://mlsanity.com/paper/2608.10790/) (2026)

## Abstract (arXiv)

Object tracking is the cornerstone of many visual analytics systems. While considerable progress has been made in this area in recent years, robust, efficient, and accurate tracking in real-world video remains a challenge. In this paper, we present a hybrid tracker that leverages motion information from the compressed video stream and a general-purpose semantic object detector acting on decoded frames to construct a fast and efficient tracking engine. The proposed approach is compared with several well-known recent trackers on the OTB tracking dataset. The results indicate advantages of the proposed method in terms of speed and/or accuracy.Other desirable features of the proposed method are its simplicity and deployment efficiency, which stems from the fact that it reuses the resources and information that may already exist in the system for other reasons.