SpeedNet: Learning the Speediness in Videos (2004.06130v2)

Abstract: We wish to automatically predict the "speediness" of moving objects in videos---whether they move faster, at, or slower than their "natural" speed. The core component in our approach is SpeedNet---a novel deep network trained to detect if a video is playing at normal rate, or if it is sped up. SpeedNet is trained on a large corpus of natural videos in a self-supervised manner, without requiring any manual annotations. We show how this single, binary classification network can be used to detect arbitrary rates of speediness of objects. We demonstrate prediction results by SpeedNet on a wide range of videos containing complex natural motions, and examine the visual cues it utilizes for making those predictions. Importantly, we show that through predicting the speed of videos, the model learns a powerful and meaningful space-time representation that goes beyond simple motion cues. We demonstrate how those learned features can boost the performance of self-supervised action recognition, and can be used for video retrieval. Furthermore, we also apply SpeedNet for generating time-varying, adaptive video speedups, which can allow viewers to watch videos faster, but with less of the jittery, unnatural motions typical to videos that are sped up uniformly.

• The paper introduces a novel deep learning model, SpeedNet, that learns to distinguish natural motion speeds in video clips.
• It employs a modified S3D-G convolutional network with self-supervised training and spatiotemporal augmentations to avoid shortcut biases.
• Experiments on Kinetics and NFS datasets show that SpeedNet outperforms optical flow baselines and supports effective adaptive video speedup and feature extraction.

Overview of SpeedNet: Learning the Speediness in Videos

The paper presents a novel deep learning model, SpeedNet, designed to predict the "speediness" of objects in videos. The primary goal is to distinguish whether an object is moving at, faster than, or slower than its natural speed, using a binary classification framework. SpeedNet is trained in a self-supervised manner on a large dataset of natural videos, achieving this task without the necessity of manual annotations. This research is situated at the intersection of video analysis and deep learning, pursuing an innovative avenue for understanding motion dynamics in video data.

Methodology

SpeedNet employs a deep convolutional network, specifically based on the S3D-G architecture, modified to preserve temporal dimensions across layers. The network's input is video segments sampled at either normal speed or a sped-up rate. By adopting a binary classification task to differentiate clips played at normal speed from those played at twice the speed, the model essentially learns a representation of "natural" vs. "artificially accelerated" motion.

Key to the model's training is the strategic avoidance of artificial shortcuts such as compression artifacts, which could bias the network's performance. The paper outlines several techniques to counteract these artifacts, including spatial and temporal augmentations, as well as a unique same-batch training method that pairs clips at different playback speeds within the same batch.

Experimental Results

The paper provides a thorough evaluation of SpeedNet on the Kinetics and Need for Speed (NFS) datasets, demonstrating robust performance in predicting speediness. SpeedNet's accuracy outperforms baseline methods, including those based purely on optical flow, underscoring its ability to understand motion dynamics beyond mere magnitude.

One of the noteworthy contributions of this work is the application of SpeedNet in adaptive video speedup. By analyzing the model's prediction curves, the researchers developed a mechanism to variably speed up video segments according to the perceived speediness, resulting in playback that appears more natural to viewers compared to uniform speedup approaches.

Additionally, the study examines SpeedNet's utility as a self-supervised feature extractor for action recognition and video retrieval tasks. The layered representation learned through speediness prediction supports competitive performance on standard benchmarks, suggesting that the model captures significant semantic features pertinent to these tasks.

Implications and Future Directions

The implications of SpeedNet span both practical video processing applications and theoretical investigations into action recognition and motion analysis. Practically, these insights contribute to enhanced video playback experiences and refined content analysis capabilities. Theoretically, the work poses intriguing questions regarding the nature of learned motion representations and their potential extrapolations in video understanding tasks.

Looking forward, continued development in adaptive speedup technology could revolutionize video streaming platforms, enabling content to be delivered efficiently without compromising viewing quality. Further, integrating SpeedNet with other video analysis frameworks could enhance its applicability across diverse domains, including sports analytics and surveillance.

This research invites future exploration into more nuanced speediness predictions, perhaps extending to multi-faceted motion dynamics or incorporating larger datasets to refine the model's understanding. Continued advancements along these lines could markedly amplify the practical applicability and theoretical understanding of motion in video data through deep learning.