Abstract
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns, termed as regularity, using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build a fully convolutional feed-forward autoencoder to learn both the local features and the classifiers as an end-to-end learning framework. Our model can capture the regularities from multiple datasets. We evaluate our methods in both qualitative and quantitative ways - showing the learned regularity of videos in various aspects and demonstrating competitive performance on anomaly detection datasets as an application.
Overview
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The paper introduces a generative model using autoencoders for learning temporal regularities in videos, aimed at applications like video summarization and anomaly detection.
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Two methods are proposed: one with handcrafted spatio-temporal features for a fully connected autoencoder, and another with a fully convolutional autoencoder that learns features and classifiers end-to-end.
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Experiments on multiple datasets demonstrate the models' abilities in anomaly detection, highlighting irregular motions and predicting future frames.
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Contributions include demonstrating autoencoders' effectiveness in capturing temporal regularities and suggesting future integration with recurrent neural networks for enhanced performance.
Learning Temporal Regularity in Video Sequences through Autoencoders
Introduction
Temporal regularity in videos refers to the repetitive or predictable patterns of motion and appearance changes in video sequences. Learning these regularities, especially with minimal supervision, is a challenging task that finds applications in various computer vision domains such as video summarization, anomaly detection, and activity recognition. Automating the segmentation of meaningful moments in videos, amidst undefined 'meaningfulness' and scene clutters, makes the problem more intricate. This paper introduces a generative model approach leveraging autoencoders, renowned for their efficiency in learning data representations with little to no supervision. Two novel methods are proposed: one utilizing handcrafted spatio-temporal local features to learn a fully connected autoencoder, and the other leveraging a fully convolutional feed-forward autoencoder to learn both local features and classifiers in an end-to-end framework.
Learning Motions on Handcrafted Features
Initial experiments use histograms of oriented gradients (HOG) and histograms of optical flows (HOF) extracted from video frames to form a 204-dimensional motion feature. A deep autoencoder, composed of seven fully connected layers, is trained on these features with the goal of minimizing the reconstruction error for regular motions while maximizing it for irregular ones. Sparse weight initialization and various other optimization techniques are employed to stabilize the learning process.
Learning Features and Motions
Acknowledging that handcrafted motion features may not be optimal for capturing temporal regularities, a novel approach employing a fully convolutional autoencoder is proposed. This model processes short video clips directly, eliminating the need for pre-defined feature extraction. Through data augmentation and optimized training regimes, the model learns to reconstruct regular motion patterns from input video clips, using the reconstruction error as a regularity score.
Applications and Evaluation
The effectiveness of the proposed methods is evaluated qualitatively and quantitatively across multiple datasets, including CUHK Avenue, Subway, and UCSD Pedestrian datasets. The models demonstrate competitive performance in anomaly detection tasks, showcasing their ability to discern irregularities in video sequences. One notable application is the generation of the most regular frame from a video sequence; objects associated with irregular motions are highlighted based on their deviation from learned regular patterns. Additionally, the model's capability of predicting past and future regular frames from a single seed image showcases its potential in video analysis applications.
Contributions and Future Directions
The main contributions of this work include the demonstration of autoencoders' effectiveness in learning temporal regularities in video sequences and developing an end-to-end model that learns optimal motion features for capturing these regularities. Furthermore, the model's application in anomaly detection and future frame prediction opens new avenues for research in video analysis. Future work could explore the integration of recurrent neural networks to enhance the model's ability to capture long-term dependencies in video sequences.
Conclusion
This paper presents a novel approach to learning temporal regularity in video sequences through autoencoders. By leveraging both handcrafted and learned motion features, the proposed models effectively capture regular patterns in video data. The methodologies and findings have significant implications for video analysis tasks, particularly in anomaly detection and predictive modeling.
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