Semi-supervised Anomaly Detection

Semi-supervised Anomaly Detection | State-of-the-Art

Generative Neural Networks for Anomaly Detection in Crowded Scenes

H. Snoussi, Tian Wang, Meina Qiao, Zhiwei Lin, Ce Li, Zhe Liu, Chang Choi•Tue Apr 30 2019

Security surveillance is critical to social harmony and people’s peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE, for anomaly detection from video data. The <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (<inline-formula> <tex-math notation="LaTeX">$S_{F}$ </tex-math></inline-formula>-VAE) and a Skip Convolutional VAE (<inline-formula> <tex-math notation="LaTeX">$S_{C}$ </tex-math></inline-formula>-VAE). The <inline-formula> <tex-math notation="LaTeX">$S_{F}$ </tex-math></inline-formula>-VAE is a shallow generative network to obtain a model like Gaussian mixture to fit the distribution of the actual data. The <inline-formula> <tex-math notation="LaTeX">$S_{C}$ </tex-math></inline-formula>-VAE, as a key component of <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both <inline-formula> <tex-math notation="LaTeX">$S_{F}$ </tex-math></inline-formula>-VAE and <inline-formula> <tex-math notation="LaTeX">$S_{C}$ </tex-math></inline-formula>-VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE is evaluated using four public datasets. The experimental results show that the <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE outperforms the state-of-the-art algorithms. The code is available publicly at <uri>https://github.com/tianwangbuaa/</uri>.

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Real-World Anomaly Detection in Surveillance Videos

Deep Semi-Supervised Anomaly Detection

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

Learning Temporal Regularity in Video Sequences

Deep Weakly-supervised Anomaly Detection

Generative Neural Networks for Anomaly Detection in Crowded Scenes

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

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks