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This study examines the relationship between H.264 video compression and theperformance of an object detection network (YOLOv5). We curated a set of 50surveillance videos and annotated targets of interest (people, bikes, andvehicles). 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 compressedvideos and detection performance was analyzed at each CRF level. Test resultsindicate that the detection performance is generally robust to moderate levelsof compression; using a CRF value of 37 instead of 22 leads to significantlyreduced bitrates/file sizes without adversely affecting detection performance.However, detection performance degrades appreciably at higher compressionlevels, especially in complex scenes with poor lighting and fast-movingtargets. Finally, retraining YOLOv5 on compressed imagery gives up to a 1%improvement in F1 score when applied to highly compressed footage.

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