Case Sentosa in Singapore, ARVAS Case Sentosa in Singapore, ARVAS Case Sentosa in Singapore, ARVAS Case Sentosa in Singapore, ARVAS , Abnormality Recognition Video Analytic System (ARVAS), Abnormality Recognition Video Analytic System (ARVAS) is a flagship product of Vi Dimensions. It differentiates from other conventional video analytics software in that it employs a selflearning algorithm which does not require the surveillance operator to pre-define any rules for real-time detection. The system uses GPU for massive hardware scalability, allowing Artificial Intelligence and Machine Learning to be effectively deployed for auto-detecting abnormal events in real-time. The use of Deep Neural Networks also allows the human operator to provide feedback to the system in order to manage and prioritize the alarms. Instead of defining rules, we look for deviant activity patterns. The system can now discover all kinds of abnormal behaviours (even those we do not know beforehand). ARVAS is an AI-assisted video anomaly detection system that requires no input of rules or pre-configuration. It continuously analyses video streams and highlights any abnormal behaviour to operators in real time. ARVAS is the future of smart surveillance. Using unsupervised machine learning, ARVAS can automatically learn what's normal & what's not - flagging out an unlimited variation of anomalous behaviors happening on site. ARVAS is highly compatible with any existing CCTV infrastructure and easy to deploy without any setup of rules or much configuration. This allows security control room operators to focus on reviewing anomalous events flagged out to them, giving them advanced situational awareness of the sites and transforming security surveillance to be proactive. ARVAS has been integrated with Nx Witness, allowing alerts to be viewed and recorded on Nx Witness for easy investigation and review. The ARVAS system utilizes machine learning to automatically analyze hours of data to find potential threats and unusual patterns. It reduces false alarms and increases the detection of potential threats based on the detection of new anomalies. With machine learning, the system simultaneously analyzes multiple moving figures such as in crowded areas. All this can be done without the user specifying rules for finding abnormalities and threats, which greatly reduces the need for a human operator to monitor every scene. ARVAS can be integrated with existing cameras, video management and video analytics systems to effectively enhance the overall surveillance network. Learning from its environment, the system can pick up on incidences such as aggressive behavior or suspicious activities like loitering or someone carrying unusually large objects. The AI can also help increase safety by spotting mischievous behaviors like children jumping over barricades, barriers or entering unsafe and restricted zones. By further increasing productivity for operators, the AI will automatically list the top 10 abnormal events based on the camera or geographic location so the user can focus on the next step of action.
