Name
Abstract | ||
|---|---|---|
Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients. Then we map the TSC with a special type of stacked Recurrent Neural Network (sRNN). By taking advantage of sRNN in learning all parameters simultaneously, the nontrivial hyper-parameter selection to TSC can be avoided, meanwhile with a shallow sRNN, the reconstruction coefficients can be inferred within a forward pass, which reduces the computational cost for learning sparse coefficients. The contributions of this paper are two-fold: i) We propose a TSC, which can be mapped to a sRNN which facilitates the parameter optimization and accelerates the anomaly prediction. ii) We build a very large dataset which is even larger than the summation of all existing dataset for anomaly detection in terms of both the volume of data and the diversity of scenes. Extensive experiments on both a toy dataset and real datasets demonstrate that our TSC based and sRNN based method consistently outperform existing methods, which validates the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICCV.2017.45 | 2017 IEEE International Conference on Computer Vision (ICCV) |
Keywords | Field | DocType |
reconstruction coefficients,sparse coefficient learning,abnormal video events,feature extraction,parameter optimization,nontrivial hyper-parameter selection,stacked Recurrent Neural Network,Temporally-coherent Sparse Coding,stacked RNN framework,anomaly detection | Anomaly detection,Pattern recognition,Computer science,Neural coding,Recurrent neural network,Feature extraction,Artificial intelligence,Linear programming,Encoding (memory) | Conference |
Volume | Issue | ISSN |
2017 | 1 | 1550-5499 |
ISBN | Citations | PageRank |
978-1-5386-1033-6 | 41 | 1.02 |
References | Authors | |
20 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weixin Luo | 1 | 92 | 8.23 |
| Wen Liu | 2 | 49 | 3.57 |
| Shenghua Gao | 3 | 1607 | 66.89 |