# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 6 Discussion
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref** & **Crowd Monitoring** &
\begin{tabular}{c} **Methods/** \\ **Techniques** \\ \end{tabular} & **Datasets** & **Research focus** & **Accuracy** \\ \hline
[25] & \begin{tabular}{c} People counting \\ and localization \\ \end{tabular} & \begin{tabular}{c} SD-CNN \\ model \\ \end{tabular} & \
\begin{tabular}{c} UCSD, world \\ Expo’10 and \\ UCF-CC-50 \\ \end{tabular} & Count and localize &
\begin{tabular}{c} Reduced the \\ classification time \\ \end{tabular} \\ \hline
[43] & \begin{tabular}{c} Crowd counting \\ and localization \\ \end{tabular} & DISAM & UCSD and World expo’10 & \
\begin{tabular}{c} Detection, estimation \\ and localization \\ \end{tabular} &
\begin{tabular}{c} Reduced the \\ classification time \\ \end{tabular} \\ \hline
[57] & \begin{tabular}{c} Crowd counting \\ and localization \\ \end{tabular} & \begin{tabular}{c} SPN+12 SM \\ \end{tabular} & \
\begin{tabular}{c} ShanghaiTech A,B, \\ UCF-CC-50 \\ and UCF-QNRF \\ \end{tabular} &
\begin{tabular}{c} Large variation in \\ density for crowd \\ counting \\ \end{tabular} & 4.2\%, 14.3\%, 27.1\% and 20.1\% rates of MAE \\ \hline
[11] & \begin{tabular}{c} Estimation and \\ localization \\ \end{tabular} & \begin{tabular}{c} Composition loss \\ \end{tabular} & \begin{tabular}{c} Shanghai Tech Part A, \\ Shanghai Tech B, \\ UCF-CC-50 and \\ UCF-QNRF data sets \\ \end{tabular} & \
\begin{tabular}{c} Counting, estimation \\ of density map \\ and localization. \\ \end{tabular} &
\begin{tabular}{c} Decreased the \\ error rate of \\ compositional \\ loss. \\ \end{tabular} \\ \hline
[60] &
\begin{tabular}{c} Crowd managing \\ and monitoring \\ \end{tabular} & Deep CNN & Subway-carriage scenes & Crowd density estimation & 91.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CNN and deep CNNfirstly, to propose density estimation of the crowd and secondly, to count the number of people using K-Gaussian Mixture Model (GMM). A new model has been proposed namely identifiable crowd monitoring (iCrowd) using three layers approach i.e., device layer, middleware layer and application layer to identify and monitor the crowd [80]. A Feature From Accelerated Segment Test (FAST) algorithm is introduced in [81] to detect and estimate the number of people in a crowd. The deep learning frameworks like CNN and Long Short Term Memory (LSTM) have been used in [58] for crowd density estimation. In [38] an expert crowd control and management system for haji has been used with three strategies i.e., to address congestion and overcrowded situation using; First In First Out (FIFO), priority queuing and Weighted Round Robin (WRR). An automatic multiple human detection method using hybrid adaptive Gaussian mixture model was introduced in [82] for human detection. The efficiency of proposed method has further evaluated and analyzed by using Receiver Operating/Output Characteristics (ROC), Mean Absolute Error (MAE) and Mean Relative Error (MRE). The proposed method has shown better results. The crowd can also be monitored and counted by using mobile phones and adopting clustering methods. Through these methods the model has achieved 92% accuracy [83]. The airborne camera systems with the techniques of Support Vector Machine (SVM) and Gabor filters have also been used in [84] for crowd monitoring and density estimation. The quality of results clearly depends upon good training samples and similar images. In [40], the authors have developed a decision support system and information management module for the real time crowd density measurements. This model has also been implemented for the crowd monitoring during Haji. In [44], the authors have presented a framework for crowd behavior using an Enhanced Context-Aware Framework and achieved experimental results with the accuracy of 99.1% and 2.8% of False Negative Rate (FNR) indicating a significant improvement over the 92.0% accuracy and FNR of 31.3% of the Basic Context-Aware Framework (BCF).