## 2 Literature Survey  
[PERSON] et al. [11] introduced new encoder-decoder CNNs that merge the feature maps in both decoding and encoding sub-networks for generating a more reasonable density map and predicting the people count very precisely. Moreover, the authors present an innovative evaluation technique termed the Patch Absolute Error (PAE) which is a suitable method to measure the accurateness of density maps. [PERSON] et al. [12] modelled an MDTL-ICDDC method to detect objects. This MDTL-ICDDC method focused on the effectual classification and identification of CD on video surveillance systems. The MDTL-ICDDC method mainly uses an SSA in addition to the NASNetLarge method as a feature extractor where the hyper-parameter tuning will be executed by the SSA. In addition, for CD and classification process, a weighted ELM (WELM) technique was used.  
[PERSON] et al. [13] modelled a trivial CNN related CD estimation method through the merging of the dilated convolution modified and MobileNetv2. In [14], a novel testing process that depends on features from accelerated segment test (FAST) techniques was presented for detecting the crowd features from drone imageries captured through different camera positions and orientations. In [15], utilizing the Hough circle transformation, a CD estimation technique was modelled. In the technical background and foreground, data was divided by leveraging the ViBe technique along with the segmentation of foreground data. Such segmented foreground data can be employed in the Hough circle transformation for CD estimation.  
In [16], the authors were interested in implementing ML for crowd management for monitoring populated areas and to thwart congestion circumstances. The authors devised a Single-CNN including the Three Layers (S-CNN3) method for counting people in a scene and make a conclusion of crowd estimation. Afterward, a comparative analysis for density counting accomplishes the efficiency of this presented technique towards the CNNs with Switched CNN (SCNN) and four layers (single-CNN4). [PERSON] et al. [17] present a multi-linear rank support tensor machine (MRSTM) considering a tensor collection as input to the issue of predicting the CD level. Moreover, an alternative SVM technique was modelled for training an MRSTM method.  
[PERSON] et al. [18] present a new approach utilized for correctly analyzing the crowd stability dependent upon images achieved in a real-time video surveillance system (VSS) from dense crowd conditions. For enhancing the accuracy of human head detection to the crowd count and CD estimate, the authors enhance the CNN approach with further columns and merged features, achieving a four-column CNN (4C-CNN). [PERSON] et al. [19] project a model which is integrated dual-modal data; the video surveillance streams and transportation scheduling data for predicting the future CD in the transportation buildings. This technique employs the temporal convolutional layer for extracting the time dependence of video streams and transportation schedules. The predictions fuse the data or utilize the GRU layers for predicting the CD.  
In [20], a Multi-Step CD Predictor (MSCDP) for fusing video frame structures and equivalent density heatmaps is presented for correctly forecasting future CD heatmaps. For capturing long-term periodic movement features, the long-term optical flow context memory (LOFCM) element was planned for storing learnable patterns. In [21], the authors initially established a WiFi monitor recognition which captures smartphone passive Wi-Fi signal data comprising MAC address and RSSI. Afterward, the authors present a positioning technique dependent upon the smartphone passive WiFi probe and a dynamic fingerprint management approach. Therefore, the authors designed a computing model for the probability of a user creating one WiFi signal for identifying people populations. Lastly, the author presents a CD estimate solution dependent upon a WiFi probe packet positioning technique.