Does Video Compression Impact Tracking Accuracy?
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 2 Feb 2022.
Builds on
10 of 22 references.
References · 10
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2018YOLOv3: An Incremental Improvementcited by 2,652
- 2016Simple Online and Realtime Trackingcited by 862
- 2016Performance Measures and a Data Set for Multi-Target, Multi-Camera Trackingcited by 685
- 2016MOT16: A Benchmark for Multi-Object Trackingcited by 531
- 2019Towards Real-Time Multi-Object Trackingcited by 311
- 2016Understanding How Image Quality Affects Deep Neural Networkscited by 118
- 2017Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Trackingcited by 18
- 2018MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detectioncited by 6
- 2021SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequencescited by 2
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2022A Coding Framework and Benchmark towards Low-Bitrate Video Understandingcited by 17
- 2024SMC++: Masked Learning of Unsupervised Video Semantic Compressioncited by 11
- 2024Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Taskscited by 0
- 2026BLUE: Semantics-Preserving Video Compression for Efficient Vision-Language Surveillance Analyticscited by 0
- 2026BLUE: Semantics-Preserving Video Compression for Efficient Vision-Language Surveillance Analytics21 Jul 2026
- 2024SMC++: Masked Learning of Unsupervised Video Semantic Compression7 Jun 2024
- 2024Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks25 Mar 2024
- 2022A Coding Framework and Benchmark towards Low-Bitrate Video Understanding6 Feb 2022