Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source...Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source often significantly impacts end-to-end(E2E)latency.However,designing an optimized GOP structure to reduce this effect remains a significant challenge.This paper presents two key contributions.First,it explores how the GOP length at the video source influences E2E latency in mainstream cloud streaming services.Experimental results reveal that the mean E2E latency increases linearly with longer GOP lengths.Second,this paper proposes EGOP(an Enhanced GOP structure)that can be implemented in streaming media servers.Experiments demonstrate that EGOP maintains a consistent E2E latency,unaffected by the GOP length of the video source.Specifically,even with a GOP length of 10 s,the E2E latency remains at 1.35 s,achieving a reduction of 6.98 s compared to Volcano-Engine(the live streaming service provider for TikTok).This makes EGOP a promising solution for low-latency live streaming.展开更多
针对机场鸟类识别过程中存在识别难度较大、准确率较低等问题,该文提出了一种改进ResNet的SA-ResNet(SPDConv and Attention-ResNet)模型。模型采用空间到深度卷积(SPDConv)替换ResNet18中的跨步卷积层,避免信息的过度丢失,增强模型特...针对机场鸟类识别过程中存在识别难度较大、准确率较低等问题,该文提出了一种改进ResNet的SA-ResNet(SPDConv and Attention-ResNet)模型。模型采用空间到深度卷积(SPDConv)替换ResNet18中的跨步卷积层,避免信息的过度丢失,增强模型特征提取能力;使用高效通道注意力(ECA)改进卷积块注意力模块(CBAM),并提出高效卷积块注意力模块(ECBAM)进一步提高模型识别准确率。通过自建的ADB-20机场鸟类数据集验证表明,SA-ResNet模型的准确率达到了95.9%,能够很好地识别机场鸟类,为机场开展鸟击防范工作奠定基础。展开更多
基金supported by Henan Province Major Science and Technology Project(241100210100).
文摘Over the past few years,video live streaming has gained immense popularity as a leading internet application.In current solutions offered by cloud service providers,the Group of Pictures(GOP)length of the video source often significantly impacts end-to-end(E2E)latency.However,designing an optimized GOP structure to reduce this effect remains a significant challenge.This paper presents two key contributions.First,it explores how the GOP length at the video source influences E2E latency in mainstream cloud streaming services.Experimental results reveal that the mean E2E latency increases linearly with longer GOP lengths.Second,this paper proposes EGOP(an Enhanced GOP structure)that can be implemented in streaming media servers.Experiments demonstrate that EGOP maintains a consistent E2E latency,unaffected by the GOP length of the video source.Specifically,even with a GOP length of 10 s,the E2E latency remains at 1.35 s,achieving a reduction of 6.98 s compared to Volcano-Engine(the live streaming service provider for TikTok).This makes EGOP a promising solution for low-latency live streaming.
文摘针对机场鸟类识别过程中存在识别难度较大、准确率较低等问题,该文提出了一种改进ResNet的SA-ResNet(SPDConv and Attention-ResNet)模型。模型采用空间到深度卷积(SPDConv)替换ResNet18中的跨步卷积层,避免信息的过度丢失,增强模型特征提取能力;使用高效通道注意力(ECA)改进卷积块注意力模块(CBAM),并提出高效卷积块注意力模块(ECBAM)进一步提高模型识别准确率。通过自建的ADB-20机场鸟类数据集验证表明,SA-ResNet模型的准确率达到了95.9%,能够很好地识别机场鸟类,为机场开展鸟击防范工作奠定基础。
