# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 3 Crowd Monitoring (CM) Approaches  
Table 2 shows completely about all the research papers about crowd counting, localization and behavior detection. Different research papers with a keyword \"crowd monitoring\" have been searched while using two mainly used databases i.e., web of science and scopus from 2014-2019, respectively. We have found different methods and techniques related to crowd monitoring and tried to review it completely. Further the elaboration of the crowd counting, localization and behavior have been deliberately discussed in tabular form as shown in Table 2.  
Figure 1: (**a**) The list of research papers published in web of science and scopus on crowd monitoring from 2014–2019 while (**b**) shows machine learning on crowd monitoring  
Figure 2: Crowd related research approaches. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref** & **Process** & **Frameworks/Methods** & **Performance** & **Conclusion/Result** \\ \hline
[4] & Crowd monitoring (Counting) & Combination of crowd size estimation and counting & Accuracy 90\% & Classification with MSE 0.0081 \\ \hline
[37] & Crowd monitoring (Counting) & Neural network and regression trees using fisheye camera & BPNN provides the best estimation & BPNN can deal 9 frames in second \\ \hline
[38] & Crowd monitoring (Estimation) & ICrowd framework was designed on a three-layer approach, device layer, middlelayer and application layer & No experimental results & Capable of location updates \\ \hline
[39] & Density estimation (Estimation) & Airborne camera systems, support vector machine and Gabor filter & Depends on good training samples and similar images & Gabor filter plays a prominent role in real scenes of images \\ \hline
[40] & Crowd monitoring (Controlling crowd movement) & Information management module and decision support system & Expert system module performs well & Real-time crowd density measurements and communications during hajj \\ \hline
[41] & Crowd Counting (Normal/abnormal event) & Median filter and Kalman filter & Accuracy 95.5\% & Robust smart surveillance system \\ \hline
[1] & Estimation and localization(Density map) & Deep CNN networks & Specificity 75.8\% & Decrease error rate \\ \hline
[42] & Detection and localization (Anomaly detection) & Global and local descriptors, with two classifiers were proposed & Accuracy 99.6\% & Achieved good and competing methods with low computational complexity \\ \hline
[43] & Counting and Localization (Human heads) & CNN & DISAM outperforms for UCSD and WorldExpo’10 datasets with the lowest MAE of 1.01 and 8.65, respectively & Reduction of classification time \\ \hline
[25] & Counting and localization & SD-CNN Model & Average Precision and average recall rate 73.58 and271.68 & Reduction of classification time and improvement in detection accuracy \\ \hline
[44] & Crowd monitoring(Behavior) & EHCAF & 99.1\% accuracy and FNR of 2.8\%, & Highly accurate and low FNR \\ \hline
[45] & Crowd monitoring (Behavior detection) & Isometric mapping (ISOMAP) & Reduced feature space & Reduction of computation time \\ \hline \end{tabular}
\end{table} 
Table 2: Crowd Monitoring (CM) approaches.