AnomalyNet: An Anomaly Detection Network for Video Surveillance

By Joey Tianyi Zhou, Jiawei Du, Hongyuan Zhu, Xi Peng, Y. Liu, Rick Siow Mong Goh. IEEE Transactions on Information Forensics and Security, 2019

317 citations recorded by the source corpus.

Sparse coding-based anomaly detection has shown promising performance, of which the keys are feature learning, sparse representation, and dictionary learning. In this paper, we propose a new neural network for anomaly detection (termed AnomalyNet) by deeply achieving feature learning, sparse representation, and dictionary learning in three joint neural processing blocks. Specifically, to learn better features, we design a motion fusion block accompanied by a feature transfer block to enjoy the advantages of eliminating noisy background, capturing motion, and alleviating data deficiency. Furthermore, to address some disadvantages (e.g., nonadaptive updating) of the existing sparse coding optimizers and embrace the merits of neural network (e.g., parallel computing), we design a novel recurrent neural network to learn sparse representation and dictionary by proposing an adaptive iterative hard-thresholding algorithm (adaptive ISTA) and reformulating the adaptive ISTA as a new long short-term memory (LSTM). To the best of our knowledge, this could be one of the first works to bridge the $\ell _{1}$ -solver and LSTM and may provide novel insight into understanding LSTM and model-based optimization (or named differentiable programming), as well as sparse coding-based anomaly detection. Extensive experiments show the state-of-the-art performance of our method in the abnormal events detection task.

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

View Paper: https://www.semanticscholar.org/paper/b20e564edbef25009fa1baa2c369437c89147e61

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. Chunhua Shen
2. Anton van den Hengel
3. Guansong Pang
4. Jianfeng Feng
5. Longbing Cao
6. Weishi Zheng
7. Fa-Ting Hong
8. Damien Teney

In Computer Networks and Communications

1. Guansong Pang
2. Chunhua Shen
3. Jianfeng Feng
4. Longbing Cao
5. Weishi Zheng
6. Anton van den Hengel
7. Fa-Ting Hong
8. Licheng Jiao

In Computer Vision and Pattern Recognition

1. Chunhua Shen
2. Anton van den Hengel
3. Xiaogang Wang
4. Luc Van Gool
5. Dacheng Tao
6. Yi Yang
7. Weishi Zheng
8. Wanli Ouyang

Citations in the corpus

• MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection (2021, methodology): “[33, 16, 7, 17, 15, 5, 36] , which encodes the usual pattern with only normal training samples, and then detects the distinctive encoded patterns as anomalies.”
• Deep Learning for Anomaly Detection (2020, methodology): “Similarly, the ResNet models pre-trained on MNIST can empower abnormal event detection in various video surveillance datasets [117, 176].”