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 |