RETRACTED ARTICLE: Crowd analytics: literature and technological assessment

In previous years, the world has gone through several natural disasters like Tsunami, earthquakes, floods, tornadoes, hurricanes, cyclones, etc., and manufactured disasters such as stampedes, fire, terror attacks, etc. A large number of causalities are reported, with a massive loss to life, economy, and other things. Knowing this, we should make a transit from a reliable and flexible disaster management approach to a proactive one by leveraging advances in science and technology. A colossal increase in worldwide population points out that the occurrence of a crowd at any place is becoming more and more familiar with each passing day. It is undeniable that these mass gatherings often become a source of a crowd-related catastrophe such as sudden escape, terror attacks, mob lynching, human stampede, or human crushing. Prior research on the crowd’s social, psychological, and computational dynamics has indicated that the crowd’s behavior under such devastating conditions is greatly decisive for crowd safety, its access or escape from the affected region, and emergency evacuation. Despite this, there is a certain paucity of pragmatic research on extreme crowd-related use cases and how to deal with such situations effectively. Through the past years, people and media have shared the details of such happenings and their experiences on a micro-level through various social network mediums. Attempts are being made to analyze this data using advanced technological tools and methods to extract the trends out of such happenings and predict any future happenings so that countermeasures can be taken and they can be prevented. This paper makes a structured literature assessment on the current scenario and systematically surveys the studies made in this field. It paves the path for future rendezvous in this area to unearth the hidden gold mine of information along the timeline. Also, an attempt is made to develop a technological solution or system that may help achieve an elevated level of social security via holistic video surveillance capable of detecting any crowd-related anomaly and proactively warning the concerned authorities about any such casualty. This will ensure that crowd disasters can be prevented well in time by gaining prior insights about them. The system is developed that encompasses everything from human detection, tracking, and counting to any abnormal behavior detection. The same has been achieved with 93.33% accuracy.