Anomaly detection based on deep support vector data description under surveillance scenarios

Fangli Li

doi:10.3788/IRLA20210094

Journals >Infrared and Laser Engineering >Volume 50 >Issue 9 >Page 20210094 > Article

Infrared and Laser Engineering
Vol. 50, Issue 9, 20210094 (2021)

Anomaly detection based on deep support vector data description under surveillance scenarios

Fangli Li

Author Affiliations

School of Information Engineering, Jiangxi University of Technology, Nanchang 330098, China

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DOI: 10.3788/IRLA20210094 Cite this Article

Fangli Li. Anomaly detection based on deep support vector data description under surveillance scenarios[J]. Infrared and Laser Engineering, 2021, 50(9): 20210094 Copy Citation Text

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Fig. 1. The flow chart of video anomaly detection based on DSVDD

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Fig. 2. Examples of the detection results

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Method	Avenue	ShanghaiTech Campus
Conv-AE^[15]	80.0%	60.9%
Stacked RNN^[21]	81.7%	68.0%
Unmasking^[23]	80.6%	－
Davide et al.^[24]	－	72.8%
Object-centric auto-encoders^[25]	86.5%	78.5%
MemAE^[26]	83.3%	72.2%
New Baseline^[27]	85.1%	72.8%
Ours	87.4%	74.5%

Table 1. AUC scores of the anomaly detection results

Fangli Li. Anomaly detection based on deep support vector data description under surveillance scenarios[J]. Infrared and Laser Engineering, 2021, 50(9): 20210094

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