## 3 The Proposed Framework
### 3D Residual Convolutional Networks  
The 2D-CNNs perform convolution and pooling operations only spatially, while 3D-CNN is used for spatio-temporal representations. Features from 3D convolutional networks encapsulate information related to shapes and motions in sequence of images, which can be useful for abnormal event detection. Besides, [PERSON] et al. in [30] proved that their C3D features are separated remarkably compared to 2D based model, which can be desirable for videos representations. Figure 1 shows how it can be effective to perform clustering with features generated by C3D than those generated by ImageNet [36] on UCF101 dataset.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline Trajectory analysis & Effective for detecting deviant trajectories in non-crowded scenes & Occlusion-sensitive especially in crowded scenes. Inability to detect abnormal shapes without deviant trajectories \\ \hline Handcrafted features & Effective for simple local shape or motion & Does not link between local patterns, not optimal for complex events \\ \hline Deep learning: Supervised & Effective for behavior understanding High capacity on image processing & Need to use normal and abnormal examples during the training phase \\ \hline Deep learning: Unsupervised & Effective for behavior understanding High capacity on image processing Does not require normal and abnormal training examples \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of some methods of abnormal events detection in terms of advantages and disadvantages.  
The 3D-CNNs proposed in [30; 31; 32] proved to be highly competitive compared to different 2D networks in various video analysis tasks, such as scene, object or action recognition.  
The use of pretrained 2D convolutional network such as features extractors for abnormal event detection was recently developed by [18; 26]. However, it was proven in [30], that image-based feature extraction is not suitable for action recognition, since the 2D convolution does not consider temporal relationship between successive frames. For instance, in [26], a sequence of frames was used as an input of the CNN. However, the convolution kernels in the 2D-CNN does not allow consideration of temporal information. i.e., after the first convolutional layer, the movement information is destroyed by the 2D convolution. That is why the use of multiple frames as an input of the 2D neural networks is not adequate for temporal features extraction.  
Furthermore, when training a network with a very large number of layers, the gradient required to update the weight with backpropagation becomes smaller when reaching earlier layers. This is the so-called vanishing gradient problem. As a result, as the network goes deeper, its performance gets saturated or even starts degrading rapidly. That is why [PERSON] et al. in [14] proposed a novel architecture based on residual blocks (see Figure 2). The original output mapping F(x) is readjusted into \(F(x)+x\). It is thus easy to optimize the residual mapping than the first one.  
This network named \"ResNet\" is formed of 152 layers. It has obtained the first place in the Large Scale Visual Recognition Challenge (ILSVRC) 2015 challenge with a classification error of 3.6%.