## 3 The Proposed Framework
### Anomaly Detection
#### 3.2.2 Classifier
(2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Plug-and-play cnn for crowd motion analysis: An application in abnormal event detection. _arXiv_**2016**, arXiv:1610.00307. * _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 13-16 December 2015; pp. 4489-4497. * _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Convnet architecture search for spatiotemporal feature learning. _arXiv_**2017**, arXiv:1708.05038. * _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON] Learning spatio-temporal representation with pseudo-3d residual networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22-29 October 2017; pp. 5534-5542. * _[PERSON] (1963)_ [PERSON] Pattern recognition using generalized portrait method. _Autom. Remote Control_**1963**, _24_, 774-780. * _[PERSON] and [PERSON] (2011)_ [PERSON]; [PERSON] LIBSVM: A library for support vector machines. _ACM Trans. Intell. Syst. Technol. (TIST)_**2011**, \(2\), 27. [CrossRef]
* _[PERSON] (1998)_ [PERSON] A tutorial on support vector machines for pattern recognition. _Data Min. Knowl. Discov._**1998**, \(2\), 121-167. [CrossRef]
* _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22 nd ACM International Conference on Multimedia, Orlando, FL, USA, 3-7 November 2014; pp. 675-678. * _[PERSON] (2008)_ [PERSON]; [PERSON] Visualizing data using t-SNE. _[PERSON]. Learn. Res._**2008**, \(9\), 2579-2605. * _[PERSON] et al. (2000)_ [PERSON]; [PERSON]; [PERSON] The mahalanobis distance. _Chemom. Intell. Lab. Syst._**2000**, _50_, 1-18. [CrossRef]
* _[PERSON] et al. (2009)_ [PERSON]; [PERSON]; [PERSON] Abnormal crowd behavior detection using social force model. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA, 22-24 June 2009; pp. 935-942. * [PERSON] et al. (2008) [PERSON]; [PERSON]; [PERSON]; [PERSON] Robust real-time unusual event detection using multiple fixed-location monitors. _IEEE Trans. Pattern Anal. Mach. Intell._**2008**, _30_, 555-560. [CrossRef] [PubMed]
* [PERSON] et al. (2012) [PERSON]; [PERSON] [PERSON]; [PERSON] Multi-scale and real-time non-parametric approach for anomaly detection and localization. _Comput. Vis. Image Underst._**2012**, _116_, 320-329. [CrossRef]
* [PERSON] and [PERSON] (2009) [PERSON]; [PERSON] Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA, 22-24 June 2009; pp. 2921-2928. * [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON]; [PERSON] Anomaly detection in crowded scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, 13-18 June 2010; pp. 1975-1981. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder. _Electron. Lett._**2016**, _52_, 1122-1124. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep-cascade: Cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. _IEEE Trans. Image Process._**2017**, _26_, 1992-2004. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Fast and accurate detection and localization of abnormal behavior in crowded scenes. _Mach. Vis. Appl._**2017**, _28_, 965-985. [CrossRef]
* [PERSON] (1951) [PERSON]; [PERSON]. _Discriminatory Analysis-Nonparametric Discrimination: Consistency Properties_; Technical Report; University of California: Berkeley, CA, USA, 1951. * [PERSON] et al.