Name
Abstract | ||
|---|---|---|
Security surveillance is critical to social harmony and people's peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called S2-VAE, for anomaly detection from video data. The S2-VAE consists of two proposed neural networks: a... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIFS.2018.2878538 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
Convolutional neural networks,Feature extraction,Anomaly detection,Gaussian distribution,Guassian processes,Mixture models,Video surveillance | Anomaly detection,Autoencoder,Pattern recognition,Computer science,Feature extraction,Gaussian,Artificial intelligence,Generative grammar,Artificial neural network | Journal |
Volume | Issue | ISSN |
14 | 5 | 1556-6013 |
Citations | PageRank | References |
6 | 0.42 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tian Wang | 1 | 21 | 6.47 |
| Meina Qiao | 2 | 14 | 1.30 |
| Zhiwei Lin | 3 | 69 | 14.95 |
| Ce Li | 4 | 6 | 1.43 |
| Hichem Snoussi | 5 | 509 | 62.19 |
| Zhe Liu | 6 | 287 | 54.56 |
| Chang Choi | 7 | 261 | 39.04 |