Unsupervised Generic Event Boundary Detection in Videos using Motion Cues

Core Concepts

Unsupervised algorithmic methods leveraging optical flow can outperform supervised neural network models for generic event boundary detection in videos.

Abstract

The paper proposes FlowGEBD, an unsupervised and non-parametric approach for generic event boundary detection in videos. It introduces two algorithms:

Pixel Tracking (PT): This method tracks a sparse set of pixels across frames using sparse optical flow and identifies event boundaries based on significant changes in the number of tracked pixels.

Flow Normalization (FN): This method computes dense optical flow for each frame, aggregates the maximum flow for each patch, normalizes the flow over time, and identifies event boundaries based on high normalized flow values.

The authors conduct extensive experiments on the challenging Kinetics-GEBD and TAPOS datasets. Key findings:

FlowGEBD, the ensemble of PT and FN, achieves state-of-the-art results among unsupervised methods on Kinetics-GEBD, outperforming supervised neural network baselines.
FlowGEBD obtains an F1@0.05 score of 0.713 on Kinetics-GEBD, a 31.7% absolute gain over the unsupervised baseline.
On the TAPOS dataset, FlowGEBD achieves an average F1 score of 0.623, an 8% improvement over the unsupervised baseline.
The proposed methods are non-parametric, computationally efficient, and robust to threshold variations, making them suitable for real-world applications.

Stats

Video has accounted for 82.5% of all web traffic in 2023, making it the most popular form of content on the internet.
The Kinetics-GEBD dataset contains 54,691 videos of 10 seconds each, spanning a broad spectrum of video domains.
The TAPOS dataset contains 1,790 instances of Olympic sports videos for the validation set.

Quotes

"Generic Event Boundary Detection (GEBD) task aims to recognize generic, taxonomy-free boundaries that segment a video into meaningful events."
"Our method FlowGEBD achieves state-of-the-art results among unsupervised methods compared to non-parametric and parametric benchmarks."
"FlowGEBD exceeds the neural models on the Kinetics-GEBD dataset by obtaining an F1@0.05 score of 0.713 with an absolute gain of 31.7% compared to the unsupervised baseline."

Key Insights Distilled From

What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection

by Sourabh Vasa... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.18935.pdf

Deeper Inquiries

Can the proposed algorithms be extended to incorporate high-level semantic features from neural networks to further improve the performance on specific event localization tasks

The proposed algorithms, Pixel Tracking and Flow Normalization, can indeed be extended to incorporate high-level semantic features from neural networks to further improve performance on specific event localization tasks. By integrating features extracted from pre-trained neural networks, such as ResNet or Transformer models, the algorithms can gain a deeper understanding of the content in the videos. These features can provide information about objects, actions, and scenes, enabling the algorithms to better identify specific events and boundaries. By combining motion cues with semantic features, the algorithms can achieve a more comprehensive understanding of the video content, leading to improved performance in event localization tasks.

How can the temporal refinement stage be enhanced to better handle rare and popular event boundaries

To enhance the temporal refinement stage for handling rare and popular event boundaries more effectively, several strategies can be implemented. One approach is to incorporate a dynamic thresholding mechanism that adapts to the distribution of boundaries in the video sequence. By analyzing the density of boundaries and the temporal distance between them, the algorithm can adjust the threshold dynamically to capture both rare and popular boundaries accurately. Additionally, introducing a clustering algorithm that considers the temporal proximity and similarity of boundaries can help group together related boundaries, improving the overall refinement process. By combining these techniques, the temporal refinement stage can better handle a variety of event boundary scenarios, including rare and popular boundaries.

What other motion-based cues or representations could be explored to complement the optical flow-based approach for more robust event boundary detection

In addition to optical flow-based approaches, several other motion-based cues or representations can be explored to complement the event boundary detection process. One potential cue is motion energy, which quantifies the overall movement in a video segment. By analyzing the distribution of motion energy across frames, the algorithm can identify segments with significant motion changes, indicating potential event boundaries. Another cue is motion directionality, which captures the direction of movement in the video. By analyzing the consistency and changes in motion direction, the algorithm can detect transitions between different actions or scenes, leading to more accurate event boundary detection. Furthermore, exploring motion coherence, which measures the consistency of motion patterns within a video segment, can help identify coherent events and boundaries. By integrating these additional motion cues with optical flow-based approaches, the algorithm can enhance its robustness and accuracy in detecting event boundaries.