Safe city

Application

Missing person search, tracking down criminals, and collecting statistics. Applied on city streets, public spaces, entertainment and shopping centers. Top priority is to never miss a wanted individual, even if it may lead to false identifications.

Use case requirements

• Detecting the maximum number of faces in the frame
• Dense flow of people in the frame (~1 person/m²)
• People in the frame not facing the camera or slowing down for identification
• Human speed in the captured frame up to 5 km/h (individuals moving at a standard walking pace)
• Frames captured under changing lighting and weather conditions, with camera lenses subject to dirt or obstruction
• Head rotation angle in relation to the camera lens not exceeding 40° horizontally and 20° vertically
• Image type for detection and identification is "WILD" (according to NIST), which corresponds to QAA totalscore >= 40%

Recommended configuration files

• safety_city_q1.xml
• safety_city_q2.xml

How to configure

1. Open the ./cfg/image-api.values.yaml file in Image API distribution, find the capturer configuration object (path to the object: processing.services.service name.configs.capturer) and enter the same values for the fields of the capturer object in each detection service: face-detector-face-fitter, face-detector-liveness-estimator, face -detector-template-extractor.

Example:

configs:
    capturer:
        name: safety_city_q2.xml // name of the Face SDK configuration file

2. After editing the file, save it and update Image API in the cluster using the command:

./cli.sh image-api install

Benchmark results

Capturer configuration file	Time to detect one frame (ms)	Detection accuracy (0 to 1)
safety_city_q1.xml	1350	0.74
safety_city_q2.xml	370	0.685