1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
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
© Copyright 2021 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.