## 2 Related Works  
This section presents a detailed literature review of the existing crowd density analysis models. [PERSON] et al. [14] proposed a novel encoder-decoder CNN that combines the feature map in encoding and decoding subnetworks for estimating the number of people accurately. In addition, the authors present a new assessment methodology called the Patch Absolute Error (PAE) that is more applicable for measuring the accuracy of density maps. [PERSON] et al. [15] resolve crowd density evaluation problems for dense and sparse conditions. Consequently, it generates two contributions: (1) a network called Patch Scale DiscriminantRegression Network (PSDR). Considering an input crowd image, it splits the images as two patches and sends them into a regression network, which then yields a density map. It fuses the two patch density maps in order to predict the whole density map as the output. (2) A person classification activation map (CAM) technique is the other contribution. In [16], the authors developed a Wi-Fi monitoring detection system that could capture smart phone passive Wi-Fi signal data involving a received signal strength indicator and MAC address. Next, the authors present a positioning model based on a dynamic fingerprint management strategy and smart phone passive Wi-Fi probe. In real time social activities, an individual might possess zero, one, two, or many smart phones with different Wi-Fi signals. Thus, it can be designed as a methodology for calculating the possibility of users generating one Wi-Fi signal to recognize the total population of people. Last, the authors developed a crowd density evaluation method based on a Wi-Fi packet positioning model. In [17], the current research progression on density estimation and crowd counting has been comprehensively analyzed. First, the authors present the background of density estimation and crowd counting. Next, they summarized the traditional crowd counting method. Later, the authors focus on investigating the density estimation and crowd counting methodologies based on a CNN model. In [18], the authors proposed a crowd density estimation model, utilizing Hough circle transformation. Here, background and foreground datasets were segregated by ViBe technology and the segmentation of foreground datasets. In [PERSON] et al. [19], a crowd density estimation method in an aerial image is proposed for examining a crowded region that shows an abnormal density. The presented technique comprises an inference and offline phase. The offline phase focused on generating a crowd model with a combination of handcrafted and relevant deep features designated by the use of the minimum-redundancy maximum-relevance (mRMR) method. During the inference phase, the previously generated models for classifying the aerial image patches yield the following four classifications: None, Sparse, Medium, and Dense. In [20], the network compression to the CNN-based crowd density estimation method is applied for reducing their computation and storage costs. In particular, the authors depend on l1-norm for selecting insignificant filters and physically pruning them. These models are trained to identify the insignificant filter and to increase the regression performance simultaneously. The authors in [21] developed a new crowd density estimation model by the use of DL models for passenger flow recognition model in exhibition centers. At the initial stage, the difference amplitude feature and gray feature of the central pixel are derived to create the CLBP feature for obtaining more crowd-group description information. Moreover, the LR activation function is used for adding the non-linear factors to the CNN and exploiting dense blocks derived from crowd density estimation for calibrating the LR-CNN crowd density estimation model. [PERSON] et al. [22] developed a fully convolutional neural network (FCNN) model for crowd density estimation on surveillance video captured by a camera at a distance. [PERSON] et al.