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

@article{Kiran2018AnOO,
  title={An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos},
  author={Bangalore Ravi Kiran and Dilip Mathew Thomas and Ranjith Parakkal},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.03149}
}
Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. 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. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal… 
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264 Citations
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264 Citations

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Improved anomaly detection in surveillance videos based on a deep learning method
  • A. Khaleghi, M. Moin
  • Computer Science
    2018 8th Conference of AI & Robotics and 10th RoboCup Iranopen International Symposium (IRANOPEN)
  • 2018
TLDR
The goal of this article is to propose a new method based on deep learning techniques for anomaly detection in video surveillance cameras, evaluated in the UCSD dataset, and showed an increase in the accuracy of the anomaly detection.
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods
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A novel and deep look at existing methods and results in stating the shortcomings of these approaches are provided, which can be a hint for future works.
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Self-Supervised Representation Learning for Visual Anomaly Detection
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This work considers the problem of anomaly detection in images and videos, and presents a new visual anomaly detection technique for videos that identifies the frame indices of a jumbled video sequence allowing it to learn the spatiotemporal features of the video.
A New Semantic and Statistical Distance-Based Anomaly Detection in Crowd Video Surveillance
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This work investigates a new hybrid visual embedding method based on deep features and a topic model for anomaly detection that demonstrates the effectiveness of this proposed method in anomaly detection.
Anomaly detection in video
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Novel approaches for learning motion features and modelling normal spatio-temporal dynamics for anomaly detection using deep convolutional neural networks and a sequence-to-sequence encoder-decoder for prediction and reconstruction are presented.
Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild
TLDR
The proposed approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm increases the distance between the classification scores of anomalous and normal videos, reducing the number of false negatives.
Anomaly Detection in Videos Using Two-Stream Autoencoder with Post Hoc Interpretability
TLDR
A two-stream approach is introduced that offers an autoencoder-based structure for fast and efficient detection to facilitate anomaly detection from surveillance video without labeled abnormal events and post hoc interpretability of feature map visualization is presented to show the process of feature learning.
Anomaly residual prediction with spatial–temporal and perceptual constraints
TLDR
This work proposes an anomaly detection algorithm based on the state-of-the-art prediction framework, leveraging the gap between frame prediction and its ground truth to detect abnormal events, and develops a new perceptual constraint focusing on high-level information.
Spatio-Temporal Unity Networking for Video Anomaly Detection
TLDR
This study proposes a novel spatio–temporal U-Net for frame prediction using normal events and abnormality detection using prediction error, which combines the benefits of U-Nets in representing spatial information with the capabilities of ConvLSTM for modeling temporal motion data.
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