## 3 The Proposed Framework
### Anomaly Detection
#### 3.2.2 Classifier
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **EERFL** & **EERPL** \\ \hline [PERSON] [39] & 42 & 80 \\ [PERSON] [40] & 42 & 76 \\ [PERSON] [41] & 30 & / \\ [PERSON](MPCCA) [42] & 30 & 71 \\ [PERSON] [15] & 24.40 & / \\ [PERSON](MDT) [43] & 24 & 54 \\ [PERSON] [16] & 21.7 & / \\ [PERSON] [7] & 20 & / \\ [PERSON] [27] & 19 & 24 \\ [PERSON] [24] & 18.50 & 29.90 \\ [PERSON] [29] & 18 & / \\ [PERSON] (AMDN double fusion) [28] & 17 & / \\ [PERSON] [44] & 15 & / \\ [PERSON] (GAN) [17] & 14 & / \\ [PERSON](BC) [22] & 13 & 26 \\ [PERSON](STC) [9] & 13 & 26 \\ [PERSON] [25] & 12 & / \\ [PERSON] [23] & 10 & 17 \\ [PERSON] [26] & 11 & 15 \\ [PERSON] [45] & 8.2 & 19 \\ [PERSON] [46] & 7.5 & 16 \\
**SC1** & **6.25** & **9.82** \\
**SC2** & **7.45** & **9.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Equal Error Rates for frame and pixel level comparisons on Ped2 Dataset. The EER values for the different methods were extracted from the literature. Figure 8: Receiver operating characteristic (ROC) curve of the proposed method. The blue and green curves are for the first and second scenarios, respectively. The Area Under the Curve (AUC) values for the two scenarios (SC1 and SC2) are 86% and 84%, respectively

1.1% when the operator includes only one false alarm. Besides, in Folder 7 when the operator declares 5 false alarms, the EER decreases by 5.6%. When dealing with the computational time, the comparison of our method with other state of the art methods is presented in Table 5, the results are only indicative and should be treated with caution. This is mainly due to the different hardware performances of each work. In our case, we used I7 CPU with 32 Gb of RAM and graphic card NVIDIA Quadro 2000 M. The proposed computational time presented in Table 5 is not optimal and does not reflect the full potential of our method. The main objective was to minimize the error and to propose a robust method for real-world application. ## 5 Conclusions

In this paper, a novel online adaptive method based on combination of pretrained 3D residual network and online classifier were developed and implemented. It is able to detect abnormal events, prevent the marginalization of normal behavior that rarely occurs during the training phase and adapt to the appearance of new normal events in the testing phase. In addition, our method does not require pretreatment methods such as tracking or background subtraction. This method is based on two main stages: Spatiotemporal feature extraction without any need of training, and the use of robust incremental classifier that prevents the redundancy of information in CCTV. It can also either be used online or offline. We have tested our proposed methodology on two main datasets using crowded (Ped2) and non-crowded scenes (CapSec). The results from the Ped2 dataset showed high performance in detection and localization for abnormal events based on The EERFL and EERPL. To the best of our knowledge, this method outperforms all existing techniques present in the literature and used for Ped2. Besides, the fastness and the simplicity of this method allow us to use it for real-world application (case of CapSec). The results presented in this paper showed the effectiveness of using this framework in detecting abnormal events. This method is robust, takes into account rare normal events present in the training phase. Besides, it can be incorporated in online CCTV. Moreover, the method can be adapted so that human operators select false alarms to prevent its future appearance, which is suitable for dynamic environment. In this method, the localization of abnormal events is reflected as patches in the original image. In some cases, these patches may overflow on normal regions. In future work, we will focus on the detection at the pixel level to precisely localize the abnormal regions.