# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 6 Discussion
The detection of anomalous crowd behavior has been monitored in [45] using Isometric Mapping (ISOMAP). During the monitoring and detecting of crowd behavior the ISOMAP has reduced the computational time significantly. To quantify the crowd behavior analysis a spatio-temporal model has been proposed in [46] on CUHK and UMN data sets. The accuracy achieved for both data sets are 98% and 88%, respectively. A software named as legion Evac has been used in [47] for the behavior of crowd evacuation, during simulating legion Evac calculates various metrics that reflect a holistic pattern of crowd evacuation, which capture the behavior of crowd. In [85], authors have developed a probabilistic detection of crowd events (running, walking, splitting, merging, local dispersion and evacuation) on Optical Flow Manifolds (OFM) using Optical Flow Vector (OFV) and Optical Flow Bundles (OFB). Dealing with the issue of different behaviors captured in surveillance video for the use of normal and abnormal behavioral detection, clustering based group analysis has been used in [48] and described certain group behavior, such as collectivity, uniformity and conflict. In [49], the authors have proposed a Spatial Temporal Texture (STT) model which can automate and identify crowd behavior under complex real life situation. The anticipated STT method demonstrates similar results of Spatio-Temporal Composition (STC) and Inference by Composition (IBC) and used less time and a smaller amount of system memory resources. In [50], the authors have presented an unsupervised abnormal crowd behavior detection using approximate median filter and foreground segmentation algorithms. In [51] the authors have proposed a model that may detect and identify the violent behavior of crowd using hybrid random matrix and deep neural network. In [52], the authors presented a novel method for detecting crowd behavior in video sequences using probability model of speed and direction. This method comprises of two main phases; building the motion model (speed and direction) and comparing the model of different frames to detect anomalies. A Scale Invariant Feature Transform (SIFT) technique is used in [1] for detection of crowd behavior in real time video sequences. Similarly, a fixed width clustering algorithm and YOLO have been used in [53] to detect the crowd behavior in video surveillance. In [54] the authors have suggested an effective and concrete method for detecting abnormalities on the basis of optical flow path of the joint points for each human body. The method has an expressively higher detection rate on the public data set with 87.4% accuracy. In [41], the authors have presented a unique multiperson tracking system for crowd counting and normal/abnormal indoor and outdoor monitoring system using median and Kalman filters, and have obtained 95.5% accuracy in event detection. In [42] the authors proposed a system for identification and localization of anomalies in crowded sense in real time. The performance was calculated on the basis of its accuracy i.e., 99.6%. Similarly, in [43] the authors have proposed a novel model namely Density Independent and Scale Aware model for crowd counting and localization in highly dense crowd and evaluated the model on Mean Absolute Error (MAE). In [25] the Scale Driven Convolutional Neural Network model has proposed to count and localize the crowd. This strategy reduced the classification time significantly and improved the detection accuracy. There are many common problems in research related to crowd monitoring such as scale variation, complex background, localization, etc. which need to be solved by using different techniques. For scale variation, SD-CNN has been proposed in literature with the assumption that the head is the only visible feature in the crowd. In a dense crowd, the issue of scale variation has been addressed by generating a scale aware object proposal. Similarly, for large density variation, learning to scale model has also been proposed.