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- W4309043291 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." @default.
- W4309043291 created "2022-11-21" @default.
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- W4309043291 date "2022-11-10" @default.
- W4309043291 modified "2023-09-25" @default.
- W4309043291 title "Impact of Video Compression on the Performance of Object Detection Systems for Surveillance Applications" @default.
- W4309043291 doi "https://doi.org/10.48550/arxiv.2211.05805" @default.
- W4309043291 hasPublicationYear "2022" @default.
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