# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 1 Introduction  
Crowd is same or different set of people arranged in one group and motivated by common goals. There are two types of crowd namely structured crowd and unstructured crowd. In the former, the direction of the movement is towards a common point and people are not in scattered form while in the later type the direction of the people is not towards a common point and they are usually in scattered form [1]. Crowd monitoring has a wide range of applications such as, safety monitoring, disaster management, traffic monitoring and design of public spaces. This variety of applications have encouraged researchers throughout numerous fields to develop models for crowd monitoring and associated tasks such as counting [2; 3; 4; 5], density estimation [6; 7; 8], tracking [9], scene understanding [10], localization [11] and behavior detection [12; 13]. Among these, the crowd counting and density estimation are important tasks and represent fundamental building blocks for several other applications [14]. There are three most commonly used methods for crowd counting namely, object detection based counting [15; 16; 17], clustered based counting [18] and regression based counting [19; 20]. In object detection based counting; the object detectors are trained to localize the position of every person in the crowd for counting. Cluster based crowd counting consists of identifying and tracking visual features. Feature trajectories that show coherent motion are clustered and the numbers of cluster is regarded as a estimation of moving objects [21]. Regressionbased counting estimates the crowd count by carrying out regression between the image features and crowd size. Though the regression based methods are working good in situations of high density as they capture generalized density information from the image of crowd, still it has two main limitations i.e., performance degradation due to overestimating of count in low density situations and improper distribution of crowd in the scene [22]. One may consider that conventional crowd counting methods such as patch based approach, object based approach and rich feature approach [8; 23; 24], which depend on either detection or regression, are limited when handling real scene with unavoidable density variations. However, these methods can only be used to estimate and count the people in low density situations in which all parts of the people are fully visible. The performance of these methods deteriorates when applied in high density situations. An ideal counting method should have the adaptive ability to choose the appropriate counting mode according to crowd density [20]. Exact crowd counting and localization are indispensable for handling high density crowds. Localization means to get the accurate location of the heads in an image. Head is the only visible part through which localization can be found in highly dense crowd images or videos [25]. Although numerous steps have been taken in the detection of human heads [16; 17; 26], head detection is still a challenging task. As a result of the variation in the scale appearance of heads, it is still a big problem to exactly discriminate human heads from the background. For localization in crowded scene, density map has been used as a regularizer during the detection [27]. In computer vision, crowd behavior detection in video surveillance is one of the latest research areas [1]. Crowd behavior detection has many application domains such as automatic detection of riots or chaotic acts in crowd and localization of abnormal regions [28]. Detection of crowd behavior is extensively used in order to monitor and maintain the surveillance of public places like sports events, markets, religious and political gatherings, etc. There are two categories of crowd behavior detection namely global crowd behavior detection and local crowd behavior detection [29].