Remembering history with convolutional LSTM for anomaly detection

By Weixin Luo, Wen Liu, Shenghua Gao. IEEE International Conference on Multimedia and Expo, 2017

531 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/792250ae660b7c25f85eeea7dcae623e4301d97c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Anton van den Hengel
2. Guansong Pang
3. Chunhua Shen
4. Dongze Lian
5. Muhammad Mohsin Waqas
6. Usa Wannasingha Humphries
7. Jongyoun Noh
8. Hyunjong Park

In Computer Networks and Communications

1. Muhammad Mohsin Waqas
2. Usa Wannasingha Humphries
3. Guansong Pang
4. Dilip Ravi Kiran
5. Mathew Thomas
6. Ranjith Parakkal
7. Chunhua Shen
8. Joey Tianyi Zhou

In Computer Vision and Pattern Recognition

1. Anton van den Hengel
2. Chunhua Shen
3. Shenghua Gao
4. Y. Liu
5. Hongyuan Zhu
6. Joey Tianyi Zhou
7. Dacheng Tao
8. Jindong Wang

Citations in the corpus

• A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction (2021, methodology): “We compare our proposed HF2-VAD with state-of-the-art methods, including (1) reconstruction-based methods: Conv-AE [11], ConvLSTMAE [31], GMFC-VAE [7], MemAE [8] and MNADR [38]; (2) prediction-based methods: Frame-Pred.”
• Learning Memory-Guided Normality for Anomaly Detection (2020, background): “, a convolutional AE (Conv-AE) [9], a 3D Conv-AE [50], a recurrent neural network (RNN) [29, 26, 25], and a generative adversarial network (GAN) [33].”
• Latent Space Autoregression for Novelty Detection (2018, unknown): “609 ConvLSTM-AE [26] 0.”
• An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos (2018, methodology): “In [60], a convolutional representation of the input video is used as input to the convolutional LSTM and a de-convolution to reconstruct the ConvLSTM output to the original resolution.”
• AnomalyNet: An Anomaly Detection Network for Video Surveillance (2019, methodology): “We compare our method with some state-of-the-art approaches including two handcrafted features based methods [7], [66] and six deep learning based methods [3], [5], [14], [34], [37], [39], [64] on these two datasets.”
• Future Frame Prediction for Anomaly Detection - A New Baseline (2017, methodology): “Thus, by taking both advantages of CNN and RNN, [5][21] leverage a Convolutional LSTMs Auto-Encoder (ConvLSTM-AE) to model normal appearance and motion patterns at the same time, which further boosts the performance of the Conv-AE based solution.”
• Deep Learning for Anomaly Detection (2020, methodology): “The most straightforward way is to follow the same procedure as the conventional use of AEs by adapting the network architecture to the type of input data, such as CNN-AE [56, 172], LSTM-AE [96], Conv-LSTM-AE [92] and GCN (graph convolutional network)-AE [37].”
• Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, background): “tional long short-term memory (ConvLSTM) can model spatial-temporal correlations by using convolutional layers instead of fully connected layers, some researchers [15], [37] add ConvLSTM layers to autoencoder, which better encodes the change of appearance for normal data.”
• A critical review of RNN and LSTM variants in hydrological time series predictions (2024, methodology): “Convolutional LSTM (ConvLSTM) networks enhance the regular LSTM [ 87 ] by introducing convolutional elements into the LSTM design to handle spatiotemporal data [ 88 ], as shown in Fig.”