Robust, Deep and Inductive Anomaly Detection

Chalapathy, Raghavendra; Menon, Aditya Krishna; Chawla, Sanjay

doi:10.1007/978-3-319-71249-9_3

Cited by 128 publications

(89 citation statements)

References 31 publications

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“…Their method shows competitive performance on two datasets in comparison to existing state-of-the-art methods. Chalapathy et al [5] present a robust CAE that considers anomalies in the data and modifies the objective function for training the model. They use the reconstruction error as a score and comment that their method is not over-sensitive to anomalies; can discover subtle anomalies and can be potentially deployed in live settings.…”

Section: Related Workmentioning

confidence: 99%

DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Nogas

Khan

Mihailidis

2019

J Healthc Inform Res

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Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Moreover, in these highly skewed situations it is also difficult to extract domain specific features to identify falls. In this paper, we present a novel framework, DeepFall, which formulates the fall detection problem as an anomaly detection problem. The DeepFall framework presents the novel use of deep spatio-temporal convolutional autoencoders to learn spatial and temporal features from normal activities using non-invasive sensing modalities. We also present a new anomaly scoring method that combines the reconstruction score of frames across a video sequences to detect unseen falls. We tested the DeepFall framework on three publicly available datasets collected through non-invasive sensing modalities, thermal camera and depth cameras and show superior results in comparison to traditional autoencoder and convolutional autoencoder methods to identify unseen falls.

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Section: Related Workmentioning

confidence: 99%

DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Nogas

Khan

Mihailidis

2019

J Healthc Inform Res

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“…Inspired by [20], Hasan et al [10] apply convolutional autoencoder for reconstructing normal frames. Some follow-up works [25,2] propose to build a more robust version. Xu et al [32] use stacked de-noising autoencoders [28] and optical flow to capture both appearance and motion information.…”

Section: Related Workmentioning

confidence: 99%

Future Frame Prediction Using Convolutional VRNN for Anomaly Detection

Kumar

Nabavi

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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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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“…Chalapathy et al [32] applied deep neural network to construct robust nonlinear subspace that captures the majority of data points and detect anomaly instances, while allowing for some data to have arbitrary corruption. As the deep network extends the robust PCA model to the nonlinear autoencoder setting, the nonlinearity helped discover potentially more subtle anomalies, which promoted the robustness of the model.…”

Section: Low-rank and Sparsementioning

confidence: 99%

Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting

Tang

Pan

Zhou

2017

Mathematical Problems in Engineering

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This paper proposes an accurate crowd counting method based on convolutional neural network and low-rank and sparse structure. To this end, we firstly propose an effective deep-fusion convolutional neural network to promote the density map regression accuracy. Furthermore, we figure out that most of the existing CNN based crowd counting methods obtain overall counting by direct integral of estimated density map, which limits the accuracy of counting. Instead of direct integral, we adopt a regression method based on low-rank and sparse penalty to promote accuracy of the projection from density map to global counting. Experiments demonstrate the importance of such regression process on promoting the crowd counting performance. The proposed low-rank and sparse based deep-fusion convolutional neural network (LFCNN) outperforms existing crowd counting methods and achieves the state-of-the-art performance.

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Robust, Deep and Inductive Anomaly Detection

Cited by 128 publications

References 31 publications

DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Future Frame Prediction Using Convolutional VRNN for Anomaly Detection

Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting

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