iSentry
From AI to Action
iSentry is a full spectrum AI Analysis platform for real-time monitoring of video surveillance imagery, focused on dealing with a wide range of complex, live video environments.
Our unsupervised, self-learning networks extract relevant data from imagery through behavioral anomaly detection (Unusual Behavior) in fluid, busy environments, or advanced motion analysis (TREX) in more static surroundings where intrusion detection is required.
These alerts are then classified for better contextualization and sent to a powerful rules engine which automates part of the decision-making process.
Put simply, iSentry monitors massive volumes of CCTV footage and alerts control room when it observes something out of the ordinary.
iSentry delivers financial return on existing CCTV surveillance infrastructure that is normally only used for forensic analysis of past incidents by providing real-time analysis and response capabilities immediately.
Core capabilities
Detecting Unusual Behaviour
This is the most important iSentry algorithm. Detection of Unusual Behavior is driven by an unsupervised AI platform. AI learning is based on pixel analysis. The system ‘learns’ how objects normally move in a scene; after establishing a norm, the system creates an alert on any deviations. The amount of video to be analyzed by a monitoring control center operator is reduced by up to 98%. A single Unusual Behavior license often replaces 5-10 licenses based on Rules Based algorithms.
Threat Ranking and Extraction (TREX)
The iSentry Platform is exceptional at target acquisition thanks to its ability to learn the environment covered by cameras. iSentry accurately extracts true target information even under the most challenging of environmental conditions. It can distinguish between moving foliage and true targets so that it delivers minimal false negatives. Additionally, its proprietary neural networks perform industry leading visual or thermal camera object classification.
The platform provides expert configuration ability that eliminates environmental noise. Its multi-scene analysis detects small targets at great distance as well as accurate human tracking and classification at short distance, simultaneously on the same view. iSentry is also capable of running on PTZ cameras, providing autonomous scene stability tools and alert suppression during camera movement from a pre-set to the next position.
DeFence
We understand that every property has unique security needs. That’s why we offer customized security solutions tailored to our clients’ specific requirements. From the initial consultation to ongoing maintenance, we work closely with our clients to ensure their complete satisfaction.
Video Tripwire (indoor)
For sterile environments or specific areas of a video scene, iSentry has easy-to-use, multi-directional video tripwires to alert on all moving objects within the specified area that cross a defined line.
Left Object Detection
Left Object Detection will create an alert when an object enters a scene and remains stationary for more than a predefined amount of time or if an object is removed from a scene.
iSentry: How it works
Typically, up to 80% of alerts can be handled by the iSentry platform without the need for human intervention. The inherent risk of automation is largely mitigated by the underlying engine design where rules are applied only when their results are certain. Key to the iSentry philosophy is that any alert that fails automation will be placed in front of an operator for further investigation and decision. Together with the rules engine this drastically reduces the number of false positive alarms by as much as 50 - 70%.
Flexible Architecture
Centralised Architecture
Centralised Architecture
Centralised Architecture
iSentry’s Centralized Architecture has the advantage of low complexity taking advantage of economies of scale. In the case of a network containing many cameras, it could require extra bandwidth to ensure centralization of all video in a single central location.
Distributed Architecture
Centralised Architecture
Centralised Architecture
Distributed Architecture is suitable for a central control room monitoring several small and large distributed sites. It is not limited in terms of camera numbers with the entire processing requirement handled on site. Sites become fully autonomous, each with the capacity for their own control room, with records and data stored on site.
Distributed Architecture is suitable for a central control room monitoring several small and large distributed sites. It is not limited in terms of camera numbers with the entire processing requirement handled on site. Sites become fully autonomous, each with the capacity for their own control room, with records and data stored on site. Only alert data is transmitted to the central control room and therefore bandwidth usage is limited to just the alert and video data for each alert.
Complete Edge Architecture
Complete Edge Architecture
Complete Edge Architecture
Micro embedded device with GPU
This architecture takes advantage of a central Video Monitoring System (VMS) with distributed processing while limiting the bandwidth required to stream live video. All iSentry processing is done on the embedded edge device (such as an NVIDIA Jetson nano), including Deep Learning, importing live video from t
Micro embedded device with GPU
This architecture takes advantage of a central Video Monitoring System (VMS) with distributed processing while limiting the bandwidth required to stream live video. All iSentry processing is done on the embedded edge device (such as an NVIDIA Jetson nano), including Deep Learning, importing live video from the cameras and then sending only alert data and alert video to the control room central location.
Partial Edge Architecture
Complete Edge Architecture
Complete Edge Architecture
Micro embedded device without GPU
Only the first processing layer of iSentry is managed on the edge device (such as a Raspberry Pi) while subsequent Deep Learning and Rules Engine processing layers are managed centrally. The advantages of this architecture are that Deep Learning processing can be a fully shared resource in the Control Cen
Micro embedded device without GPU
Only the first processing layer of iSentry is managed on the edge device (such as a Raspberry Pi) while subsequent Deep Learning and Rules Engine processing layers are managed centrally. The advantages of this architecture are that Deep Learning processing can be a fully shared resource in the Control Center and that a wider range of embedded devices are supported.