Leveraging Compressed Frame Sizes For Ultra-Fast Video Classification

TL;DR

A novel approach that examines only the post-compression bitstream of a video to perform classification, eliminating the need for bitstream decoding is presented, outperforming traditional Dynamic Time Warping (DTW) algorithm by seven orders of magnitude.

Abstract

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.

Figures (5)

• Figure 1: Kullback-Leibler Divergence
• Figure 2: Effectiveness on broad video categories on YouTube
• Figure 3: Residual architecture classifier based on wang2017time
• Figure 4: Evaluating ability to discriminate individual vloggers within a channel
• Figure 5: Classification performance as a function of input size.