iCETANA USES COMPUTER VISION AND MACHINE LEARNING TO HIGHLIGHT THE 1% OF VIDEO SURVEILLANCE EVENTS THAT NEED ATTENTION

PATENTED TECHNOLOGY

The iCetana system utilises computer vision and a patented machine-learning algorithm to automatically learn the difference between normal movement patterns and abnormal exception events in real-time.  The system solves the problem of massive video surveillance data overload by only showing surveillance operators those camera views where something abnormal is occurring. 

SELF-LEARNING

Self-learns the “normal” motion patterns in a surveillance camera scene.  It learns: how and where people move, how traffic moves, any no-go zones and any changes in activity at different times of the day.  It does this all automatically with no programming required.

HIGHLIGHTING THE ABNORMAL

Once normal is established for each camera the system then detects and displays only the cameras with abnormal activity.  This means only a small fraction of video data needs to processed by operators for increased situational awareness.  Operators can assess and take immediate action to prevent events from escalating.

THREAT AND RISK REPORTING

Provides valuable threat and risk data through real-time and historical reporting.  The utilisation of the surveillance data provides valuable insight into evolving threats and risks. It gives surveillance operators and security management the ability to provide both real-time notification and aggregated local, regional and global surveillance trends.

INTEGRATING WITH EXISTING SYSTEMS

Displays the relevant camera scene to operators via iCetana’s LiveWall application, Video Management System (VMS) plugin, or smart mobile devices. iCetana interworks with existing video surveillance infrastructures increasing the return on investment and effectiveness of this infrastructure. 

 

 

 

THE PROBLEM WITH LIVE MONITORING

 

iCetana’s system solves the problem of massive video surveillance information overload by only showing surveillance operators those camera views where something abnormal is occurring - in real-time.

iCetana detects a wide-range of security, safety and operational events in real-time:

  • Suspicious behaviour
  • Unauthorised access - people and vehicles
  • Irregular movement (people / vehicles)
  • Trespass - no-go zones, perimeter fence, tunnels
  • Violence / Aggressive Behaviour / Assaults
  • Medical / security events requiring immediate response
  • Precursor events - crowd gathering / dispersal
  • Vandalism to fixed assets
  • Camera tampering
  • Fire risk
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LARGE SCALE RETAIL SURVEILLANCE

As a fast growing business, Majid Al Futtaim realized that managing large camera networks and risks across multiple sites and countries posed several challenges.

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EVOLVING THREAT AND RISK LANDSCAPE

Threat and Risk landscapes are changing and evolving faster than ever. Threats are presenting themselves in new forms with increasing risk to staff, customers and the public. Learn about the challenges faced by the video surveillance operators.

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UNIVERSITY ENHANCING SITUATIONAL AWARENESS

The main campus of Swinburne University of Technology (SUT) is located in the inner-suburb of Hawthorn. This presents various challenges for the security team to assess situations and assist in real-time and ensure the protection of its students, staff, and building facilities across its campuses - especially after hours.

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INCREASING EFFECTIVENESS OF VIDEO SURVEILLANCE

Curtin University is the largest university in Western Australia with over 47,000 students and 3,500 staff across 8 campuses in Australia and 2 internationally. The university has an on-going commitment to enhance safety for students, staff and visitors.