Virtually every business, organization and municipality around the world employ some form of video technology to enhance security, and increasingly for business intelligence to enhance operations and services. Industry analyst HIS Markit estimated that there may be as many as 1 billion CCTV cameras in use by the end of 2021. Yet while the number of cameras deployed seems to keep growing every year at a staggering pace with improved image resolution and overall capabilities, their primary purpose is for live monitoring and to record visual data for forensics. Which means that unless these cameras are being diligently monitored by security or law enforcement professionals, they have little effect in preventing potentially threatening events from unfolding.
While some improvements in sensor technology allow CCTV cameras to detect and alert system administrators when some levels of suspicious activity take place, such as when an object is left behind or object movement and direction, they still require live monitoring to analyze the situation and initiate action. Being able to understand the behavior of people and vehicles based on their specific actions, surrounding area and the events in which they are participating is the pathway to a whole new level of video intelligence and suspicious behavior detection.
viisights sets the benchmark with innovative behavioral analytics that can detect and analyze suspicious activities.
We’ve harnessed the power of Artificial Intelligence (AI), machine learning, and deep learning, to detect and understand a wide range of human and vehicle behaviors to automatically identify abnormalities and potential threats. Already deployed at numerous locations around the world today, viisights behavioral analytics are helping security operations and law enforcement agencies detect suspicious activity using CCTV before potentially dangerous events take place or would otherwise go undetected.
Event prediction is one of the main goals of viisights’ unique time series analysis. Prediction can play an effective role for appropriate decision making in different applied areas. WISE near real-time, temporal analysis technique, which is based on behavioral recognition, enables suspicious behavior detection which could potentially lead to other events of interest.
The simplest use case is loitering, which in some contexts, like near an ATM, can be indicative of planned fraud or theft. Driven by behavioral understanding algorithms, WISE can predict even more complex situations. It can detect an event of continuous unrest in a crowd. Such behavior is, in many cases, a predictive indicator for an upcoming violent activity. Through early detection, the user can take actions to prevent any potential violence. Though these use cases are timely and relevant, the potential for the predictive capabilities of WISE is far reaching and promising.