NHN ToastCAM

Development of face identification(detection) module for toast cam camera

Development time

3 Month

Manpower

3 Professionals

The Brief

Toast CAM, an integrated smart store management system that provides all services necessary for store operation, uses self-developed cameras for facility security, customer congestion analysis, and face recognition for entry/exit controls. NHN ToastCAM is seeking advancement of the face tracking modules for their existing system by applying features that extracts a representative image from multiple recognized images as well as consulting in the field of vision AI to minimize cost of cloud servers due to re-identification process.

Service

Development of face recognition(detection) module

System integration & technology transfer

흰육각형.png

1

흰육각형.png

2

흰육각형.png

3

흰육각형.png

4

Modelling & Test set generation

Vision AI Consulting

%E1%84%90%E1%85%A9%E1%84%89%E1%85%B3%E1%

Advancement of Face recognition and tracking module

To accurately track the movement of the person after facial recognition, repetitive inquiries of the recognized face are performed through communication with the cloud servers, which leads to an increase in operation cost. Therefore, system should recognize a face more quicky, extract only one representative face from multiple faces tracked, and able to specify time duration for inquiries to minimize communications with the server.

Module performance testing and integration

The module should achieve the same face tracking error rate 10% or less, and the ratio of the number of representative images extracted should be adjustable according to the number of pictures taken consecutively. Exclude overlapping and high-speed movement, the module should receive image sizes and formats required by AI system without affecting the performance of ToastCAM to generate results.

Technological Challenges

CCTV 카메라

Road Map

  • Development module test/ improvement

  • support education/technology transfer

small3.png
small1.png
small2.png
small4.png
  • Development of face recognition(detection) module

  • Consulting

Key Features

토스트캠.png
  • Face identification advanced module

  • Operation within AI system settings

    • Image sizes, formats

    • Representative face selection cycle

    • Face identification reset cycle

The Result

Face Identification speed: under 4FPS

Face Identification accuracy: above 90%

Fully integrated with existing system