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Deep learning (AI) algorithms for Vehicle Detection, Counting and Classification, Crowd Analytics, Facial Recognition; Aerial Analytics, ALPR, Parking Solutions, Dense Crowd Estim.
About
We use AI and deep learning technology to analyze streams of video data and turn them into meaningful information and analytics. We built a platform called Simplicity Intelligent Video Analytics ( SIVA ) that contains a collection of advanced algorithms to analyze video data for different domains such as Traffic Management, Security and Surveillance, Crowd Movement Analytics, Facial Recognition, Automatic License Plate Recognition and Video storage optimization etc.
Key Benefits
Simplicity Intelligent Video Analytics (SIVA) is amongst those who are taking video analytics to the next level using Artificial intelligence & machine learning tools. It utilizes computer software programs that analyze the images from video surveillance cameras in order to recognize humans, vehicles or objects of interest. SIVA has so far developed deep learning algorithms for Vehicle Detection, Counting and Classification; People detection and counting; Crowd Analytics; Facial Recognition; Event Detection for video summarization; Automatic License Plate Recognition (ALPR); Object detection in aerial footage; Intrusion Detection; Parking spotter & Anomaly Detection. SIVA has the capability of centralized analytics, on premises analytics and is now stepping towards Edge analytics.
Applications
Video analytics or CCTV software has come a long way over the last few years in terms of capabilities and accessibility. In previous years, analytics were primarily needed and available to large, corporate or government systems, requiring powerful servers to run each application along with high-end infrastructure. Now, due to maturing analytic engines and the exponential increase in camera and server processing power, analytics can be used by many different kinds of users and in a variety of environments. Analytics can run on the camera (edge) or on a server running multiple video streams or multiple applications. The growing prevalence of analytics on the edge offers system flexibility and can significantly reduce the cost of the overall solution, as fewer servers are required to run the analytics. Edge based analytics can also lessen system bandwidth demands, as video can be transmitted from the camera only after being prioritized by the analytics. Due to the onboard processing power of modern IP cameras, an edge analytics based approach can also offer device-specific selection of applications.. Ultimately, analytics assist operators in making informed decisions by illuminating the unusual from the mundane, as the number of cameras being deployed and monitored continues to increase. Better analytics can give the operator more reliable information, which in turn improves response time and effectiveness. Whether it’s having more business intelligence, improved security, or better safety, video analytics has proven to be a valuable technology that can be applied to almost every industry