Infrared and Laser Engineering, Volume. 51, Issue 6, 20210680(2022)
Memory-augmented deep autoencoder model for pedestrian abnormal behavior detection in video surveillance
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Fig. 1. The flow chart of Memory AE based anomaly detection method
Table 1. Comparison with the state of the art methods in terms of AUC%
View tableView in ArticleTable 1. Comparison with the state of the art methods in terms of AUC%
Method Avenue ShanghaiTech MPPCA+ SF [ 17 ]56.2% - MDT[ 18 ]77.4% - Conv-AE [ 8 ]80.0% 60.9% Conv3D-AE[ 19 ]80.9% - Stacked RNN[ 20 ]81.7% 68.0% ConvLSTM-AE[ 21 ]77.0% - MemNormality[ 22 ]88.5% 70.5% ClusterAE[ 23 ]86.0% 73.3% AbnormalGAN[ 24 ]- 72.4% Pred+Recon[ 25 ]85.1% 73.0% Proposed method 85.7% 75.3%
Table 2. The influence of the number of memory module size on the experimental results of the Avenue data set (frame-level AUC%)
View tableView in ArticleTable 2. The influence of the number of memory module size on the experimental results of the Avenue data set (frame-level AUC%)
Size of memory module 500 1000 1500 2000 2500 Result 78.2% 85.7% 85.3% 85.7% 85.8%
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Jingbo Sun, Jie Ji. Memory-augmented deep autoencoder model for pedestrian abnormal behavior detection in video surveillance[J]. Infrared and Laser Engineering, 2022, 51(6): 20210680
Paper Information
Category: Photoelectric measurement
Received: Sep. 16, 2021
Accepted: --
Published Online: Dec. 20, 2022
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