## 6 Conclusions  
In this study, we introduced a novel BCNN attention network with a densenet121/Efficientnetv2 architecture as a transfer learning model to extract feature vectors and achieve the best accuracy rate. Both the modified multistage and multicolumn models were trained to optimize the classification network. The experiments compared our method with recent state-of-art methods and evaluated performance with metrics such as accuracy, precision, recall, F1 score and [PERSON]'s kappa values. The proposed feature extraction method significantly improved the anomaly congestion detection as shown in the comparison of Table 6. The evaluated datasets contained diverse video quality levels and different viewpoints, and the proposed model achieved a good performance consistency in all scenarios. Moreover, the detection of the abnormal activity (panic moments) also achieved good results. Future dense activity detection models need to be enhanced with an autoencoder-based approach to achieve better video activity detection. The performance measures are shown in Table 7. The clarity of the surveillance video and the distance to the viewpoint with multiview aspects need to be considered for future improvements and better detection results.  
**Author Contributions:** Conceptualization, funding acquisition, methodology, supervision, C.L. and Y.L.; data curation, formal analysis, methodology, validation, visualization, results analysis, writing--original draft, E.M.C.L.E. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Method** & **AUC ( Lawn/Mall/City)** & **AUC Total** \\ \hline \multirow{10}{*}{UMN} & [PERSON] et al. [41] & 0.9981/0.9724/0.9888 & 0.9864 \\ \cline{2-3}  & [PERSON] et al. [42] & 0.995/0.975/0.964 & 0.979 \\ \cline{2-3}  & [PERSON] et al. [43] & 0.9779/0.9223/0.9849 & 0.9617 \\ \cline{2-3}  & [PERSON] et al. [44] & 0.997/0.89/0.962 & 0.945 \\ \cline{2-3}  & [PERSON] et al. [45] & 0.992/0.986/0.979 & 0.985 \\ \cline{2-3}  & [PERSON] et al. [46] & 0.9959/0.9338/0.9808 & 0.9702 \\ \cline{2-3}  & [PERSON] et al. [47] & 0.9861/0.9864/0.975 & 0.9825 \\ \cline{2-3}  & [PERSON] et al. [48] & 0.996/98.3/99.1 & 0.99 \\ \cline{2-3}  & [PERSON] et al. [49] & 0.9808/0.9955/0.9935 & 0.990 \\ \cline{2-3}  & [PERSON] [50] & 0.998/0.992/0.995 & 0.9950 \\ \cline{2-3}  & [PERSON] et al. [51] & 0.9928/0.9885/0.9995 & 0.9936 \\ \cline{2-3}  & Our (MCMS-BCNN-Attention + densenet121) & 0.9615/0.9981/0.9056 & 0.9505 \\ \cline{2-3}  & Our (MCMS-BCNN-Attention+Efficientnetv2) & 1.00/99.25/99.33 & **0.9953** \\ \hline \multirow{10}{*}{PETS2009} & **Method** & **AUC (14-16 View\_1)** & **AUC (14-33 View\_1, View\_2,View\_3)** \\ \cline{2-3}  & [PERSON] et al. [52] & 0.94 \\ \cline{2-3}  & [PERSON] et al. (Optical flow) [53] & 0.81 \\ \cline{2-3}  & [PERSON] et al. (SFM) [53] & 0.94 \\ \cline{2-3}  & [PERSON] et al. [49] & - & 0.94 \\ \cline{2-3}  & [PERSON] et al. [51] & 0.9788 & - \\ \cline{2-3}  & [PERSON] [50] & 0.9780 & - \\ \cline{2-3}  & Our (MCMS-BCNN-Attention + densenet121) & 0.9895 & 0.9666 \\ \cline{2-3}  & Our (MCMS-BCNN-Attention+Efficientnetv2) & **1.0** & **1.0** \\ \hline \end{tabular} Bold colour indicates the highest values.  
\end{table}
Table 7: Performance comparison of the proposed approach with existing techniques on UMN and PETS2009 datasets.  
**Funding:** This research was supported by the National Nature Science Foundation of China (grant no. 61671397).  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** Benchmark datasets UCSD Anomaly Detection Dataset [33], PETS 2009 Benchmark Data [34] and Monitoring Human Activity-Action Recognition UMN [35] are publicly available.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest. The manuscript contains contributions from all authors. All authors have approved the final version of the manuscript.