Future Frame Prediction Using Convolutional VRNN for Anomaly Detection

Anomaly detection in videos aims at reporting anything that does not conform the normal behaviour or distribution. However, due to the sparsity of abnormal video clips in real life, collecting annotated data for supervised learning is exceptionally cumbersome. Inspired by the practicability of gener...

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Published in:arXiv.org 2019-10
Main Authors: Lu, Yiwei, Reddy, Mahesh Kumar Krishna, Seyed shahabeddin Nabavi, Wang, Yang
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Language:English
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Anomalies
Semi-supervised learning
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Reddy, Mahesh Kumar Krishna
Seyed shahabeddin Nabavi
Wang, Yang
description Anomaly detection in videos aims at reporting anything that does not conform the normal behaviour or distribution. However, due to the sparsity of abnormal video clips in real life, collecting annotated data for supervised learning is exceptionally cumbersome. Inspired by the practicability of generative models for semi-supervised learning, we propose a novel sequential generative model based on variational autoencoder (VAE) for future frame prediction with convolutional LSTM (ConvLSTM). To the best of our knowledge, this is the first work that considers temporal information in future frame prediction based anomaly detection framework from the model perspective. Our experiments demonstrate that our approach is superior to the state-of-the-art methods on three benchmark datasets.
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subjects Anomalies
Semi-supervised learning
title Future Frame Prediction Using Convolutional VRNN for Anomaly Detection
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