论文标题
视频压缩对监视应用的对象检测系统性能的影响
Impact of Video Compression on the Performance of Object Detection Systems for Surveillance Applications
论文作者
论文摘要
这项研究检查了H.264视频压缩与对象检测网络（Yolov5）的性能之间的关系。我们策划了一组50个监视视频和注释的感兴趣目标（人，自行车和车辆）。在集合中使用恒定速率因子（CRF）值以5质量水平编码视频{22,32,37,42,47}。将Yolov5应用于压缩视频，并在每个CRF级别分析检测性能。测试结果表明，检测性能通常是强大的，至中等水平的压缩水平。使用37的CRF值而不是22个导致比特率/文件大小可显着降低，而不会影响检测性能。但是，检测性能在较高的压缩水平下显着降低，尤其是在照明较差和快速移动目标的复杂场景中。最后，将Yolov5在压缩图像上进行的再培训时，当应用于高度压缩的素材时，F1得分提高了1％。
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.
