What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

@article{Golda2019WhatGA,
  title={What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks},
  author={Thomas Golda and Nils Murzyn and Chengchao Qu and Kristian Kroschel},
  journal={2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
  year={2019},
  pages={1-8}
}
  • T. Golda, Nils Murzyn, +1 author K. Kroschel
  • Published 8 August 2019
  • Computer Science
  • 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Anomaly detection plays in many fields of research, along with the strongly related task of outlier detection, a very important role. Especially within the context of the automated analysis of video material recorded by surveillance cameras, abnormal situations can be of very different nature. For this purpose this work investigates Generative-Adversarial-Network-based methods (GAN) for anomaly detection related to surveillance applications. The focus is on the usage of static camera setups… Expand
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References

SHOWING 1-10 OF 22 REFERENCES
Training Adversarial Discriminators for Cross-Channel Abnormal Event Detection in Crowds
TLDR
Generative Adversarial Nets (GANs), which are trained to generate only the normal distribution of the data, are proposed, which outperforms previous state-of-the-art methods in both the frame-level and the pixel-level evaluation. Expand
Abnormal event detection in videos using generative adversarial nets
TLDR
Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality Detection tasks. Expand
Future Frame Prediction for Anomaly Detection - A New Baseline
TLDR
This paper proposes to tackle the anomaly detection problem within a video prediction framework by introducing a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and is the first work that introduces a temporal constraint into the video prediction task. Expand
Stan: Spatio- Temporal Adversarial Networks for Abnormal Event Detection
TLDR
A novel abnormal event detection method with spatio-temporal adversarial networks (STAN) is proposed which achieved competitive performance compared to the state-of-the-art methods. Expand
Deep multi-scale video prediction beyond mean square error
TLDR
This work trains a convolutional network to generate future frames given an input sequence and proposes three different and complementary feature learning strategies: a multi-scale architecture, an adversarial training method, and an image gradient difference loss function. Expand
Detecting anomalous events in videos by learning deep representations of appearance and motion
TLDR
A novel double fusion framework is introduced, combining the benefits of traditional early fusion and late fusion strategies, which is extensively evaluated on publicly available video surveillance datasets including UCSD pedestian, Subway, and Train, showing competitive performance with respect to state of the art approaches. Expand
Video anomaly detection using deep incremental slow feature analysis network
TLDR
A deep incremental slow feature analysis (D-IncSFA) network is constructed and applied to directly learning progressively abstract and global high-level representations from raw data sequence, which can precisely detect global anomaly such as crowd panic. Expand
Learning Temporal Regularity in Video Sequences
TLDR
This work proposes two methods that are built upon the autoencoders for their ability to work with little to no supervision, and builds a fully convolutional feed-forward autoencoder to learn both the local features and the classifiers as an end-to-end learning framework. Expand
Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
TLDR
This work proposes end-to-end trainable composite Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to predict the evolution of a video sequence from a small number of input frames. Expand
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
TLDR
The concept of end-to-end learning of optical flow is advanced and it work really well, and faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet are presented. Expand
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