Clustering Driven Deep Autoencoder for Video Anomaly Detection
Authors:
- 16 November 2020
- book chapter
- Published by Springer Nature
- p. 329-345
Abstract
No abstract availableKeywords
This publication has 34 references indexed in Scilit:
- Detecting anomalous events in videos by learning deep representations of appearance and motionComputer Vision and Image Understanding, 2017
- Learning Temporal Regularity in Video Sequences2016
- Abnormal Event Detection at 150 FPS in MATLAB2013
- A survey on unsupervised outlier detection in high‐dimensional numerical dataStatistical Analysis and Data Mining: An ASA Data Science Journal, 2012
- Online detection of unusual events in videos via dynamic sparse coding2011
- Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillanceImage and Vision Computing, 2011
- Higher Order Contractive Auto-Encoder2011
- Extracting and composing robust features with denoising autoencoders2008
- A Duality Based Approach for Realtime TV-L 1 Optical Flow2007
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006