Learning Temporal Regularity in Video Sequences

TL;DR

This paper introduces autoencoder-based models to learn and detect regular motion patterns in videos with minimal supervision, aiding in understanding activities and identifying anomalies.

Contribution

It presents two novel autoencoder frameworks that learn video regularities from limited supervision, improving anomaly detection in video sequences.

Findings

Models effectively learn regular motion patterns across datasets.

Autoencoders achieve competitive anomaly detection performance.

Frameworks work with minimal supervision.

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.