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Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection
- Source :
- Neurocomputing. 439:256-270
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- With the improvement of public security awareness, video anomaly detection has become an indispensable demand in surveillance videos. To improve the accuracy of video anomaly detection, this paper proposes a novel two-stream spatial-temporal architecture called Two-Stream Deep Spatial-Temporal Auto-Encoder (Two-Stream DSTAE), which is composed of a spatial stream DSTAE and a temporal stream DSTAE. Firstly, the spatial stream extracts appearance characteristics whereas the temporal stream extracts the motion patterns, respectively. Then, based on the novel policy joint reconstruction error, this model fuses the spatial stream and the temporal stream to extract spatial-temporal characteristics to detect anomalies. Furthermore, since the optical flow is invariant to appearances such as color or light, we introduce optical flow to enhance the capability of extracting continuity between adjacent frames and inter-frame motion information. We demonstrate the accuracy of the proposed method on the publicly available standard datasets: UCSD, Avenue and UMN datasets. Our experiments demonstrate high accuracy, which is superior to the state-of-the-art methods.
- Subjects :
- 0209 industrial biotechnology
Event (computing)
business.industry
Computer science
Cognitive Neuroscience
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Optical flow
02 engineering and technology
Invariant (physics)
Autoencoder
Computer Science Applications
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Anomaly detection
Artificial intelligence
business
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 439
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........04d210179291603fc904a1e577d83711