Classifying videos into distinct categories, such as Sport and Music Video,
is crucial for multimedia understanding and retrieval, especially when an
immense volume of video content is being constantly generated. Traditional
methods require video decompression to extract pixel-level features like color,
texture, and motion, thereby increasing computational and storage demands.
Moreover, these methods often suffer from performance degradation in
low-quality videos. We present a novel approach that examines only the
post-compression bitstream of a video to perform classification, eliminating
the need for bitstream decoding. To validate our approach, we built a
comprehensive data set comprising over 29,000 YouTube video clips, totaling
6,000 hours and spanning 11 distinct categories. Our evaluations indicate
precision, accuracy, and recall rates consistently above 80%, many exceeding
90%, and some reaching 99%. The algorithm operates approximately 15,000 times
faster than real-time for 30fps videos, outperforming traditional Dynamic Time
Warping (DTW) algorithm by seven orders of magnitude.

通过检查视频的后压缩比特流进行分类，我们提出了一种新颖的方法，消除了对比特流解码的需求，并在性能上超越了传统的动态时间规整算法。

利用压缩帧大小实现超快速视频分类

Leveraging Compressed Frame Sizes For Ultra-Fast Video Classification

Classifying videos into distinct categories, such as Sport and Music Video,
is crucial for multimedia understanding and retrieval, especially in an age
where an immense volume of video content is constantly being generated.
Traditional methods require video decompression to extract pixel-level features
like color, texture, and motion, thereby increasing computational and storage
demands. Moreover, these methods often suffer from performance degradation in
low-quality videos. We present a novel approach that examines only the
post-compression bitstream of a video to perform classification, eliminating
the need for bitstream. We validate our approach using a custom-built data set
comprising over 29,000 YouTube video clips, totaling 6,000 hours and spanning
11 distinct categories. Our preliminary evaluations indicate precision,
accuracy, and recall rates well over 80%. The algorithm operates approximately
15,000 times faster than real-time for 30fps videos, outperforming traditional
Dynamic Time Warping (DTW) algorithm by six orders of magnitude.

视频分类中，通过研究视频的压缩比特流来代替传统方法中涉及视频解压缩的特征提取，以提高分类性能和处理速度。通过验证自定义数据集，结果表明该方法在精度、准确率和召回率方面都超过 80%，而且处理速度是真实时间的 15000 倍，比传统的动态时间规整算法效果好六个数量级。