We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
静态目标检测器(YOLO系列)型无法利用视频流中时间信息及上下文信息,导致在视频场景下的检测效果不太好(视频数据存在的画面模糊、物体遮挡、镜头高速移动、物体罕见姿势等),因为算力受限的原因无法使用视频目标检测模型(一张显卡需要处理多路视频流),现在我想改进YOLOv8s模型添加一些后处理模块来获取视频数据的时间信息和上下文信息,但是没有啥好的思路,请问一下您有一些建议或者推荐一些相应的论文(或修改代码)吗?
The text was updated successfully, but these errors were encountered:
You may refer to https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection. But it's for the offline video object detection. Classic post-processing methods for video object detection such as Seq-NMS may help you.
Sorry, something went wrong.
No branches or pull requests
静态目标检测器(YOLO系列)型无法利用视频流中时间信息及上下文信息,导致在视频场景下的检测效果不太好(视频数据存在的画面模糊、物体遮挡、镜头高速移动、物体罕见姿势等),因为算力受限的原因无法使用视频目标检测模型(一张显卡需要处理多路视频流),现在我想改进YOLOv8s模型添加一些后处理模块来获取视频数据的时间信息和上下文信息,但是没有啥好的思路,请问一下您有一些建议或者推荐一些相应的论文(或修改代码)吗?
The text was updated successfully, but these errors were encountered: