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Spatio-temporal video autoencoder with differentiable memory [article]

Viorica Patraucean, Ankur Handa, Roberto Cipolla
2016 arXiv   pre-print
We describe a new spatio-temporal video autoencoder, based on a classic spatial image autoencoder and a novel nested temporal autoencoder.  ...  The temporal encoder is represented by a differentiable visual memory composed of convolutional long short-term memory (LSTM) cells that integrate changes over time.  ...  In particular, we describe a spatio-temporal video autoencoder integrating a differentiable short-term memory module whose (unsupervised) training is geared towards motion estimation and prediction Horn  ... 
arXiv:1511.06309v5 fatcat:kkeh6puykja7fiptyf5pbtwfmi

Spatio-temporal video autoencoder with differentiable memory [article]

Viorica Patraucean, Ankur Handa, Roberto Cipolla, Apollo-University Of Cambridge Repository, Apollo-University Of Cambridge Repository
2018
We describe a new spatio-temporal video autoencoder, based on a classic spatial image autoencoder and a novel nested temporal autoencoder.  ...  The temporal encoder is represented by a differentiable visual memory composed of convolutional long short-term memory (LSTM) cells that integrate changes over time.  ...  In particular, we describe a spatio-temporal video autoencoder integrating a differentiable short-term memory module whose (unsupervised) training is geared towards motion estimation and prediction Horn  ... 
doi:10.17863/cam.26485 fatcat:v5sapkokz5hivcuqe32shneine

Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTM [article]

Hanh Thi Minh Tran, David Hogg
2022 arXiv   pre-print
In this paper, we propose a novel method for video anomaly detection motivated by an existing architecture for sequence-to-sequence prediction and reconstruction using a spatio-temporal convolutional Long  ...  Short-Term Memory (convLSTM).  ...  Another Spatio-Temporal autoencoder has been proposed for video anomaly detection [16] .  ... 
arXiv:2205.08812v1 fatcat:pwsfe32bineszd4vtp6dtt3g4q

Anomaly Detection Using Prediction Error with Spatio-Temporal Convolutional LSTM

Hanh T. M. Tran, David Hogg
2022 Journal of Science and Technology Issue on Information and Communications Technology  
In this paper, we propose a novel method for video anomaly detection motivated by an existing architecture for sequence-to-sequence prediction and reconstruction using a spatio-temporal convolutional Long  ...  Short-Term Memory (convLSTM).  ...  Another Spatio-Temporal autoencoder has been proposed for video anomaly detection [16] .  ... 
doi:10.31130/ud-jst.2022.289e fatcat:w23qdzeltbdmbatetn4psgf7gq

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

B. Kiran, Dilip Thomas, Ranjith Parakkal
2018 Journal of Imaging  
We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.  ...  This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection.  ...  A video-patch (spatio-temporal) based autoencoder was employed by [44] to reconstruct patches, with a sparse autoencoder whose average activations were set to parameter ρ, enforcing sparseness, following  ... 
doi:10.3390/jimaging4020036 fatcat:za52zspzjbewbakdordavpatvq

Anomalous Event Detection in Surveillance Videos using Spatio-temporal Autoencoders

2022 International Journal of Advanced Trends in Computer Science and Engineering  
With the advancements in computer vision and deep learning, we can now contemplate these videos to detect anomalies.  ...  In this paper, we propose to identify anomalies by using Spatiotemporal autoencoders.  ...  Therefore, many researchers turned to unsupervised methods, such as Spatio Temporal encoders, which require only unlabelled footage, with little or no anomalies to train and can predict real-world anomalies  ... 
doi:10.30534/ijatcse/2022/011132022 fatcat:qpyk634xt5h3pcynuu6he22fma

An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos [article]

B Ravi Kiran, Dilip Mathew Thomas, Ranjith Parakkal
2018 arXiv   pre-print
We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.  ...  This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection.  ...  Videos can be viewed as special case of spatio-temporal processes. A direct approach to video anomaly detection can be estimating the spatio-temporal mean and covariance.  ... 
arXiv:1801.03149v2 fatcat:u6qz7upzfbdgfaxvihpl55kdhi

Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data using Deep Learning: Early Detection of COVID-19 Outbreak in Italy

Yildiz Karadayi, Mehmet N. Aydin, A. Selcuk Ogrenci
2020 IEEE Access  
In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data.  ...  Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection.  ...  Their classification framework consists of a 3D convolutional autoencoder for learning spatio-temporal features from video frames.  ... 
doi:10.1109/access.2020.3022366 pmid:34931155 pmcid:PMC8668158 fatcat:m23b5ijr25ahfko2ktxqhx2fsa

Deep learning model based on cascaded autoencoders and one‐class learning for detection and localization of anomalies from surveillance videos

Karishma Pawar, Vahida Attar
2022 IET Biometrics  
Specifically, we used a convolutional autoencoder and a sequence-to-sequence long short-term memory autoencoder in a pipelined fashion for spatial and temporal learning of the videos, respectively.  ...  Motivated by the same, this paper addresses the problem of detection and localization of anomalies from surveillance videos using pipelined deep autoencoders and one-class learning.  ...  [26] performed spatio-temporal video segmentation and used motion and appearance information of the spatiotemporal segment.  ... 
doi:10.1049/bme2.12064 fatcat:niarkuwsbzfjrpmgl4xnrxreb4

Spatio-Temporal Deep Learning-Based Methods for Defect Detection: An Industrial Application Study Case

Lucas A. da Silva, Eulanda M. dos Santos, Leo Araújo, Natalia S. Freire, Max Vasconcelos, Rafael Giusti, David Ferreira, Anderson S. Jesus, Agemilson Pimentel, Caio F. S. Cruz, Ruan J. S. Belem, André S. Costa (+1 others)
2021 Applied Sciences  
We compare 3D autoencoders, convolutional neural networks, and generative adversarial networks (GANs) with data collected in a laboratory.  ...  Our results show that autoencoders perform poorly when trained with only non-anomalous data—which is important because class imbalance in industrial applications is typically skewed towards the non-anomalous  ...  the spatio-temporal patterns of input video sequences.  ... 
doi:10.3390/app112210861 fatcat:q6u4au2rcfbjleqjmlbv76gzye

SimVP: Simpler yet Better Video Prediction [article]

Zhangyang Gao, Cheng Tan, Lirong Wu, Stan Z. Li
2022 arXiv   pre-print
We believe SimVP can serve as a solid baseline to stimulate the further development of video prediction.  ...  The code is available at \href{https://github.com/gaozhangyang/SimVP-Simpler-yet-Better-Video-Prediction}{Github}.  ...  Spatio-Temporal video autoencoder [46] incorporates Con-vLSTM and an optical flow predictor to capture changes over time.  ... 
arXiv:2206.05099v1 fatcat:kf3oe6vof5e47d5xrlj3blld7e

Learning Human Motion Models for Long-term Predictions [article]

Partha Ghosh, Jie Song, Emre Aksan, Otmar Hilliges
2017 arXiv   pre-print
We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone.  ...  This Dropout Autoencoder (D-AE) is then used to filter each predicted pose of the LSTM, reducing accumulation of error and hence drift over time.  ...  specific spatio-temporal graphs [9, 19] .  ... 
arXiv:1704.02827v2 fatcat:ak3m3s7jprfcbdkcqcewstr4r4

Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks [article]

Eder Santana, Matthew Emigh, Pablo Zegers, Jose C Principe
2017 arXiv   pre-print
We apply the proposedmethod for object recognition with temporal context in videos and obtain better results than comparable methods in the literature, including the Deep Predictive Coding Networks previously  ...  of the previously proposed Winner-Take-All Autoencoders to sequences in time, and a new technique for initializing and regularizing convolutional-recurrent neural networks.  ...  Our contributions were threefold: 1) a scalable, end-to-end differentiable reinterpretation of the sparse spatio-temporal feature extraction in Deep Predictive Coding Networks [7] ; 2) an extension of  ... 
arXiv:1611.00050v2 fatcat:osouttaz4vgrpft7s3lea6qgli

DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder [article]

Andreas Papachristodoulou, Christos Kyrkou, Theocharis Theocharides
2021 arXiv   pre-print
We explore the space of different autoencoder architectures and evaluate them on a diverse dataset created with real and synthetic images demonstrating that by exploiting spatio-temporal information combined  ...  In order to mitigate such phenomena, we propose DriveGuard, a lightweight spatio-temporal autoencoder, as a solution to robustify the image segmentation process for autonomous vehicles.  ...  3) STAE: A spatio-temporal autoencoder with a path that also extracts features from the previous frame.  ... 
arXiv:2111.03480v1 fatcat:uygberuoynbrtcuzjujrld5fim

A Survey of the Techniques for The Identification and Classification of Human Actions from Visual Data

Shahela Saif, Samabia Tehseen, Sumaira Kausar
2018 Sensors  
Any advancements in this field are tied to advances in the interrelated fields of object recognition, spatio- temporal video analysis and semantic segmentation.  ...  The results of work in this field are used in video surveillance, automatic video labeling and human-computer interaction, among others.  ...  Figure 6 . 6 Spatio-temporal interest point detection for a walking person. Reprinted with permission from [62] .  ... 
doi:10.3390/s18113979 fatcat:cbag7fm5gnetffzdl26se5r2ge
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