# Does Video Compression Impact Tracking Accuracy?

arXiv: https://arxiv.org/abs/2202.00892 · HTML: https://mlsanity.com/paper/2202.00892/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking](https://mlsanity.com/paper/1704.02781/) (2017)
- [MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection](https://mlsanity.com/paper/1805.00107/) (2018)
- [SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences](https://mlsanity.com/paper/2112.14934/) (2021)

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

- [A Coding Framework and Benchmark towards Low-Bitrate Video Understanding](https://mlsanity.com/paper/2202.02813/) (2022)
- [SMC++: Masked Learning of Unsupervised Video Semantic Compression](https://mlsanity.com/paper/2406.04765/) (2024)
- [Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks](https://mlsanity.com/paper/2403.16338/) (2024)
- [BLUE: Semantics-Preserving Video Compression for Efficient Vision-Language Surveillance Analytics](https://mlsanity.com/paper/2607.19515/) (2026)

## Abstract (arXiv)

Everyone "knows" that compressing a video will degrade the accuracy of object tracking. Yet, a literature search on this topic reveals that there is very little documented evidence for this presumed fact. Part of the reason is that, until recently, there were no object tracking datasets for uncompressed video, which made studying the effects of compression on tracking accuracy difficult. In this paper, using a recently published dataset that contains tracking annotations for uncompressed videos, we examined the degradation of tracking accuracy due to video compression using rigorous statistical methods. Specifically, we examined the impact of quantization parameter (QP) and motion search range (MSR) on Multiple Object Tracking Accuracy (MOTA). The results show that QP impacts MOTA at the 95% confidence level, while there is insufficient evidence to claim that MSR impacts MOTA. Moreover, regression analysis allows us to derive a quantitative relationship between MOTA and QP for the specific tracker used in the experiments.