Asilla: An Artificial Intelligence Physical Security System

Security camera system x Asilla: Strengthen crime prevention with security DX

We don’t know when accidents happen, such as a person falling in an unnoticeable location, property damage at night, or violence in the area. Security guards’ monitoring capacity is limited, leading to failed cases of accident prevention.

The implementation of Asilla’s product can strengthen security 24/7. Replacing security guards, a security camera continuously monitors the facility and alerts security guards in case of abnormal activities for immediate actions.


Asilla has developed a proprietary algorithm for tracking people in time-series images. Improvement of tracking performance allows for the examination of individual detection ID, even when the target is temporarily hidden.


Thanks to an autonomous learningfunction within, each edge AI device can generate an AI model based on its independent experience in the environment. As a result, each device can act as a substitute to a section of the cerebral cortex (or the fusiform gyrus area), allowing for highly precise identification of body parts. As it learns and performs autonomously, the AI can improve its accuracy without human assistance.


Open-source systems such as OpenPose have high computation cost, leading to significant implementation cost when real-time performance is required. Asilla has succeeded in transferring information from a large high-precision model to a compact one, resulting in considerable weight reduction. Thanks to this method, one server can process around 80 cameras simultaneously.


  • 24-hour monitoring / immediate notification
    Detection is operational 24/7. When an abnormal behavior is detected, an alert is immediately sent to the management screen. At our verification site, patrol and guard activities were reduced by roughly 20%.
  • Preventing “abnormal” behaviors
    AI can learn normal behaviors to detect abnormal ones. It can detect subtle differences in human behaviors to identify signs of potential accidents and prevent them.
  • Reduction of monitoring costs
    Installation is possible from 1 camera (from 5,000 yen) and a single server can process images from up to 50 cameras. We have a support system to assist you with implementation and operation.
  • Behavior classification and notification
    AI can recognize, classify, and send alerts of abnormal and suspicious behaviors (e.g. falling, fighting, vandalism, staggering, loitering, etc.). It helps you make quick decisions on how to respond and strengthen security efficiently without increasing labor.
  • Real-time notification
    Asilla can perform high-speed processing with AI analysis and inference taking less than 1 second. You can receive immediate notification without delay, and you can check what is happening at the site with a video, allowing you to improve work productivity and DX security at sites where rapid response is necessary.


Detect behaviorsPatented

  • Fighting・Violence
  • Falling
  • Staggering
  • Loitering
  • Abnormal behaviors

    Through self-learning, AI models with optimized views for each angle are automatically generated. Detection of “abnormal behaviors” (deviations from normal behaviors) can prevent accidents.


“Behavior identification system” – Patent number: 6793383 Technology to track the same person with multiple cameras
"Behavior inference device" - Patent number: 6525181 Algorithm to detect backgrounds and adjust likelihood of behaviors
"Behavior inference device" - Patent number: 6525179 Algorithm to recognize behaviors from joint coordinate values
"Target number identification device" - Patent number: 6525180 Algorithm to track people in an area in chronological order
"Suspicious/abnormal subject detection device" - Patent number: 6647489 Detection of abnormal behaviors of people in an area
"Abnormal behavior detection device" - Patent number: 6692086 Detection of abnormal behaviors from behaviors of surrounding people
"Suspicious/abnormal subject detection device" - Publication number: 2020-091856 Detection of abnormal behaviors from behaviors of surrounding people

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