[1]WANG Qianqian,MIAO Duoqian,ZHANG Yuanjian.Abnormal event detection method based on deep auto-encoder and self-updating sparse combination[J].CAAI Transactions on Intelligent Systems,2020,15(6):1197-1203.[doi:10.11992/tis.202007003]
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Abnormal event detection method based on deep auto-encoder and self-updating sparse combination

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 6 Page number: 1197-1203 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2020-11-05
Title:
Abnormal event detection method based on deep auto-encoder and self-updating sparse combination
Author(s):
WANG Qianqian;  MIAO Duoqian;  ZHANG Yuanjian
Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai 201804, China
Keywords:
deep learning; sparse combination; auto-encoder; self-updating; abnormal event detection; convolution neural network; unsupervised learning; sparse representation
CLC:
TP391
DOI:
10.11992/tis.202007003
Abstract:
In the construction of a deep learning model for abnormal event detection, frames or optical flow are considered but the resulting accuracy and speed are not satisfactory. To address these problems, we present an algorithm based on convolutional auto-encoders and self-updating sparse combination learning, which is centered on the movement of foreground blocks. First, we use an adaptive Gaussian mixture model to extract the foreground. Using a sliding window, the foreground blocks that are moving, are filtered based on the number of foreground pixels. Three convolutional auto-encoders are then constructed to extract the temporal and spatial features of the moving foreground blocks. Lastly, self-updating sparse combination learning is applied to reconstruct the features and identify abnormal events based on the reconstruction error. The experimental results show that compared with existing algorithms, the proposed method improves the accuracy of abnormality detection and enables real-time detection.
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