Crowd Monitoring System

In this time when COVID-19 is expanding quickly, it is imperative to keep a separation from one another and avoid the group in this manner we can diminish the infection spread. Numerous individuals, deliberately or accidentally, gather and meander in the city. Screen every one of these exercises is intense. Our Smart COVID-19 Crowd Detection Camera using Computer Vision Technique will watch out for all exercises and distinguish any group on the spot. The gadget additionally can alarm the concerned authority about unnecessary social affairs. A functioning reconnaissance framework can recognize the distance among people and caution them in this manner we can diminish the spread of dangerous illnesses

Extracting Social Network Groups from Video Data Using Motion Similarity and Network Clustering

Detecting Social Network Groups from Video data acquired from surveillance cameras is a challenging problem currently being addressed by the Data Mining and Computer Vision Communities. As a part of continuing research in this area, a new graph-based post analysis approach is developed to process data obtained from the state-of-the-art Detection and Tracking systems to extract the various social groups present in it. The process of extracting social network groups is primarily divided into two tasks. The first task consists of finding a method to compute a graph that connects all the people present in the video. Motion similarity between the tracks of the people on the ground plane is used as a metric to compute the weights on the edges of the graph. The second task is to cut the graph to form groups which is done by creating a minimal spanning tree and cutting the edges with least weights. The number of cuts to be made depends on the number of groups that are present in the video. To deal with the problem of unknown number of groups, the parameter of consistency of within cluster distances is exploited and the number of groups is decided by the finding the elbow point in the plot. The method shows promising results with UCLA Courtyard Dataset Videos and Simulation systems. This work can be regarded as one of the many approaches to solve the problem of “Detecting Social Networks from Video Data” which tend to exhibit decent outcomes.Computer Science, Department o

Crowd detection and counting using a static and dynamic platform: state of the art

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms

Crowd analysis and its applications

Crowd is a unique group of individual or something involves community or society. The phenomena of the crowd are very familiar in a variety of research discipline such as sociology, civil and physic. Nowadays, it becomes the most active-oriented research and trendy topic in computer vision. Traditionally, three processing steps involve in crowd analysis, and these include pre-processing, object detection and event/behavior recognition. Meanwhile, the common process for analysis in video sequence of crowd information extraction consists of Pre-Processing, Object Tracking, and Event/Behavior Recognition. In terms of behavior detection, the crowd density estimation, crowd motion detection, crowd tracking and crowd behavior recognition are adopted. In this paper, we give the general framework and taxonomy of pattern in detecting abnormal behavior in a crowd scene. This study presents the state of art of crowd analysis, taxonomy of the common approach of the crowd analysis and it can be useful to researchers and would serve as a good introduction related to the field undertaken