# Impact of Video Compression on the Performance of Object Detection Systems for Surveillance Applications

Source: [Impact of Video Compression on the Performance of Object Detection Systems for Surveillance Applications](<https://www.sota2.com/research/paper/68fa6de71e209131cea3cd09>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:46:11.149Z

Authors: Michael O'Byrne, Vibhoothi, Mark Sugrue, Anil Kokaram

Published: 2022-11-10T19:01:06.000Z

## Abstract

This study examines the relationship between H.264 video compression and the performance of an object detection network (YOLOv5). We curated a set of 50 surveillance videos and annotated targets of interest (people, bikes, and vehicles). Videos were encoded at 5 quality levels using Constant Rate Factor (CRF) values in the set {22,32,37,42,47}. YOLOv5 was applied to compressed videos and detection performance was analyzed at each CRF level. Test results indicate that the detection performance is generally robust to moderate levels of compression; using a CRF value of 37 instead of 22 leads to significantly reduced bitrates/file sizes without adversely affecting detection performance. However, detection performance degrades appreciably at higher compression levels, especially in complex scenes with poor lighting and fast-moving targets. Finally, retraining YOLOv5 on compressed imagery gives up to a 1% improvement in F1 score when applied to highly compressed footage.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.05805>)

[PDF](<https://arxiv.org/pdf/2211.05805.pdf>)