Dual-Mode iterative denoiser: Tackling the weak label for anomaly detection

Crowd anomaly detection suffers from limited training data under weak supervision. In this paper, we propose a dual-mode iterative denoiser to tackle the weak label challenge for anomaly detection. First, we use a convolution autoencoder (CAE) in image space to act as a cluster for grouping similar...

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Bibliographic Details
Published in2020 25th International Conference on Pattern Recognition (ICPR) pp. 6742 - 6749
Main Authors Lin, Shuheng, Yang, Hua
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.01.2021
Subjects
anomaly detection
cluster
Convolution
GCN
label denoising
Neural networks
Noise reduction
Pattern recognition
Predictive models
Training
Training data
weak supervision
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Summary:Crowd anomaly detection suffers from limited training data under weak supervision. In this paper, we propose a dual-mode iterative denoiser to tackle the weak label challenge for anomaly detection. First, we use a convolution autoencoder (CAE) in image space to act as a cluster for grouping similar video clips, where the spatial-temporal similarity helps the cluster metric to represent the reconstruction error. Then we use the graph convolution neural network (GCN) to explore the temporal correlation and the feature similarity between video clips within different rough labels, where the classifier can be constantly updated in the label denoising process. Without specific image-level labels, our model can predict the clip-level anomaly probabilities for videos. Extensive experiment results on two public datasets show that our approach performs favorably against the state-of-the-art methods.
DOI:10.1109/ICPR48806.2021.9412673