Enable Vehicle and Vehicle Attribute Recognition
FindFace Multi allows you to recognize a single vehicle and its attributes.
The vehicle attributes are as follows:
license plate number (for selected countries),
Note
Currently, the following countries are supported:
Europe: Russia, Lithuania, Latvia, Estonia, Finland, Czech Republic, Serbia, Belarus, Ukraine, Moldova, Georgia, Azerbaijan, Armenia.
Asia: the UAE, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, Saudi Arabia, Vietnam, India, Pakistan, Thailand.
North America: Mexico.
South America: Argentina, Brazil.
color,
make,
model,
body style,
vehicle category,
vehicle orientation (front, rear, or side),
special vehicle type (taxi, route transport, carsharing, ambulance, police, rescue service, gas service, military, road service, other special),
vehicle weight and body size.
Note
Special vehicle recognition as well as vehicle weight and body size recognition work for selected countries only and may not work for your country. For more information please contact your manager or our support team (support@ntechlab.com).
Vehicle recognition along with the attributes choice can be configured at the installation level. This section describes how to enable vehicle and vehicle attribute recognition in case this step has been skipped during installation.
To enable recognition of vehicles and their attributes, do the following:
Specify neural network models for vehicle recognition and vehicle attribute recognition in the
/opt/findface-multi/configs/findface-extraction-api/findface-extraction-api.yaml
configuration file.Important
Be sure to choose the right acceleration type for each model, matching the acceleration type of
findface-extraction-api
: CPU or GPU. Be aware thatfindface-extraction-api
on CPU can work only with CPU-models, whilefindface-extraction-api
on GPU supports both CPU- and GPU-models.Open the
findface-extraction-api.yaml
configuration file.sudo vi /opt/findface-multi/configs/findface-extraction-api/findface-extraction-api.yaml
Specify the vehicle detector model in the
detectors -> models
section by pasting the following code:GPU
detectors: ... models: ... gustav: aliases: - car - efreitor model: detector/car.gustav_accurate.006.gpu.fnk options: min_object_size: 32 resolutions: - 256x256 - 384x384 - 512x512 - 768x768 - 1024x1024 - 1536x1536 - 2048x2048 ...
CPU
detectors: ... models: ... gustav: aliases: - car - efreitor model: detector/car.gustav_accurate.006.cpu.fnk options: min_object_size: 32 resolutions: - 256x256 - 384x384 - 512x512 - 768x768 - 1024x1024 - 1536x1536 - 2048x2048 ...
Make sure that the
objects -> car
section contains thequality_attribute: car_quality
and thebase_normalizer: facenorm/cropbbox.v2.gpu.fnk
or thebase_normalizer: facenorm/cropbbox.v2.cpu.fnk
, depending on your acceleration type:GPU
objects: ... car: base_normalizer: facenorm/cropbbox.v2.gpu.fnk quality_attribute: car_quality ...
CPU
objects: ... car: base_normalizer: facenorm/cropbbox.v2.cpu.fnk quality_attribute: car_quality ...
Specify the normalizers required for the extractors. If you need license plate recognition, specify the
carlicplate
normalizer. For other extractors, specify thecropbbox
normalizer.Normalizer
Normalizer model
Used for extractors
carlicplate
carnorm/briacon.v0.gpu.fnk
carnorm/briacon.v0.cpu.fnk
car_license_plate
cropbbox
facenorm/cropbbox.v2.gpu.fnk
facenorm/cropbbox.v2.cpu.fnk
car_license_plate_quality
,car_description
,car_quality
,car_special_types11
,car_categories
,car_orientation
,car_weight_types7
GPU
normalizers: ... models: ... carlicplate: model: carnorm/briacon.v0.gpu.fnk ... cropbbox: model: facenorm/cropbbox.v2.gpu.fnk ...
CPU
normalizers: ... models: ... carlicplate: model: carnorm/briacon.v0.cpu.fnk ... cropbbox: model: facenorm/cropbbox.v2.cpu.fnk ...
Specify the extraction models in the
extractors -> models
section, subject to the extractors you want to enable:GPU
extractors: ... models: car_categories: carattr/carattr.categories.v0.gpu.fnk car_color: '' car_container_number: '' car_description: carattr/description.v0.gpu.fnk car_emben: carrec/bottas.gpu.fnk car_license_plate: carattr/carattr.license_plate.v7.gpu.fnk car_license_plate_quality: carattr/carattr.license_plate_quality.v1.gpu.fnk car_license_plate_visibility: '' car_make: '' car_orientation: carattr/carattr.orientation.v0.gpu.fnk car_quality: carattr/carattr.quality.v0.gpu.fnk car_special_types: '' car_special_types11: carattr/carattr.special_types11.v1.gpu.fnk car_trash: '' car_weight_types7: carattr/carattr.weight_types7.v0.gpu.fnk
CPU
extractors: ... models: car_categories: carattr/carattr.categories.v0.cpu.fnk car_color: '' car_container_number: '' car_description: carattr/description.v0.cpu.fnk car_emben: carrec/bottas.cpu.fnk car_license_plate: carattr/carattr.license_plate.v7.cpu.fnk car_license_plate_quality: carattr/carattr.license_plate_quality.v1.cpu.fnk car_license_plate_visibility: '' car_make: '' car_orientation: carattr/carattr.orientation.v0.cpu.fnk car_quality: carattr/carattr.quality.v0.cpu.fnk car_special_types: '' car_special_types11: carattr/carattr.special_types11.v1.cpu.fnk car_trash: '' car_weight_types7: carattr/carattr.weight_types7.v0.cpu.fnk
The following extractors are available:
Extractor
Configure as follows
vehicle feature vector
car_emben: carrec/bottas.cpu.fnk
car_emben: carrec/bottas.gpu.fnk
license plate number
car_license_plate: carattr/carattr.license_plate.v7.cpu.fnk
car_license_plate_quality: carattr/carattr.license_plate_quality.v1.cpu.fnk
car_license_plate: carattr/carattr.license_plate.v7.gpu.fnk
car_license_plate_quality: carattr/carattr.license_plate_quality.v1.gpu.fnk
set of attributes: make / color / model / body style
car_description: carattr/description.v0.cpu.fnk
car_description: carattr/description.v0.gpu.fnk
vehicle image quality
car_quality: carattr/carattr.quality.v0.cpu.fnk
car_quality: carattr/carattr.quality.v0.gpu.fnk
special vehicle
car_special_types: carattr/carattr.special_types11.v1.cpu.fnk
car_special_types: carattr/carattr.special_types11.v1.gpu.fnk
vehicle category
car_categories: carattr/carattr.categories.v0.cpu.fnk
car_categories: carattr/carattr.categories.v0.gpu.fnk
vehicle weight and body size
car_weight_types7: carattr/carattr.weight_types7.v0.cpu.fnk
car_weight_types7: carattr/carattr.weight_types7.v0.gpu.fnk
vehicle orientation
car_orientation: carattr/carattr.orientation.v0.cpu.fnk
car_orientation: carattr/carattr.orientation.v0.gpu.fnk
Tip
To leave a model disabled, pass the empty value
''
to the relevant parameter. Do not remove the parameter itself. Otherwise, the system will be searching for the default model.extractors: ... models: car_categories: '' car_color: '' car_container_number: '' car_description: '' car_emben: '' car_license_plate: '' car_license_plate_quality: '' car_license_plate_visibility: '' car_make: '' car_orientation: '' car_quality: '' car_special_types: '' car_special_types11: '' car_trash: '' car_weight_types7: ''
Restart the
findface-multi-findface-extraction-api-1
container.sudo docker container restart findface-multi-findface-extraction-api-1
Modify the
/opt/findface-multi/configs/findface-video-worker/findface-video-worker.yaml
configuration file.In the
models
section, specify the vehicle detector, normalizer, and extractor models as shown in the example below:GPU
sudo vi /opt/findface-multi/configs/findface-video-worker/findface-video-worker.yaml models: ... detectors: ... car: fnk_path: /usr/share/findface-data/models/detector/car.gustav_normal.004.gpu.fnk min_size: 60 ... normalizers: ... car_norm: fnk_path: /usr/share/findface-data/models/facenorm/cropbbox.v2.gpu.fnk car_norm_quality: fnk_path: /usr/share/findface-data/models/facenorm/cropbbox.v2.gpu.fnk ... extractors: ... car_quality: fnk_path: /usr/share/findface-data/models/carattr/carattr.quality.v0.gpu.fnk normalizer: car_norm_quality
CPU
sudo vi /opt/findface-multi/configs/findface-video-worker/findface-video-worker.yaml models: ... detectors: ... car: fnk_path: /usr/share/findface-data/models/detector/car.gustav_normal.004.cpu.fnk min_size: 60 ... normalizers: ... car_norm: fnk_path: /usr/share/findface-data/models/facenorm/cropbbox.v2.cpu.fnk car_norm_quality: fnk_path: /usr/share/findface-data/models/facenorm/cropbbox.v2.cpu.fnk ... extractors: ... car_quality: fnk_path: /usr/share/findface-data/models/carattr/carattr.quality.v0.cpu.fnk normalizer: car_norm_quality
Make sure that the
objects -> car
section is included:objects: ... car: normalizer: car_norm quality: car_quality track_features: ''
Restart the
findface-multi-findface-video-worker-1
container.sudo docker container restart findface-multi-findface-video-worker-1
Open the
/opt/findface-multi/configs/findface-video-manager/findface-video-manager.yaml
configuration file and make sure it contains thecar
section indetectors
that looks similar to the example below. Note that thefilter_min_quality
parameter is set to0.65
by default. You can increase it to get more accurate results from cameras and other video sources. The best practice is to set this value to0.73
.sudo vi /opt/findface-multi/configs/findface-video-manager/findface-video-manager.yaml detectors: ... car: filter_min_quality: 0.65 filter_min_size: 100 filter_max_size: 8192 roi: '' fullframe_crop_rot: false fullframe_use_png: false jpeg_quality: 95 overall_only: true realtime_post_first_immediately: false realtime_post_interval: 1 realtime_post_every_interval: false track_interpolate_bboxes: true track_miss_interval: 1 track_overlap_threshold: 0.25 track_max_duration_frames: 0 track_send_history: false post_best_track_frame: true post_best_track_normalize: true post_first_track_frame: false post_last_track_frame: false tracker_type: simple_iou track_deep_sort_matching_threshold: 0.65 track_deep_sort_filter_unconfirmed_tracks: true track_object_is_principal: false track_history_active_track_miss_interval: 0
Note
Configuration of the
filter_min_quality
parameter affects the results that you get from video sources only and does not affect vehicle counting results and quality of the vehicle images that are used in the records. To get more accurate counting results or vehicle records with better quality images, configure theMINIMUM_CAR_QUALITY
parameter individually in thefindface-multi-legacy.py
configuration file. This setting will be described below.Enable recognition of vehicles and their attributes in the
/opt/findface-multi/configs/findface-multi-legacy/findface-multi-legacy.py
configuration file. Do the following:In the
FFSECURITY
section, set'ENABLE_CARS': True
.sudo vi /opt/findface-multi/configs/findface-multi-legacy/findface-multi-legacy.py FFSECURITY = { ... # optional objects to detect 'ENABLE_CARS': True, ...
In the same section, you can configure the
MINIMUM_CAR_QUALITY
parameter. By default, it’s set to0.65
, which is the recommended minimum value for vehicle image quality, sufficient to make a record or use an object in a counter.FFSECURITY = { ... # minimum car quality sufficient to add it to a card and use object in counter # p.s. set this parameter equals to `0.73` for more accurate results 'MINIMUM_CAR_QUALITY': 0.65, # model: [carattr.quality.v0] ...
If you need more accurate counting results or vehicle records with better quality images, you can increase the
MINIMUM_CAR_QUALITY
threshold. The best practice is to set this value to0.73
.In the
FFSECURITY
section, specify the attributes you want to display for vehicle recognition events.# available features are: category, description, license_plate, orientation, special_vehicle_type, weight_type 'CAR_EVENTS_FEATURES': ['license_plate', 'category', 'special_vehicle_type', 'description', 'weight_type', 'orientation'],
Restart the
findface-multi-findface-multi-legacy-1
container.sudo docker container restart findface-multi-findface-multi-legacy-1
In the web interface, navigate to Video Source. Select a camera in the Cameras tab (or an uploaded file in the Uploads tab, or an external detector in the corresponding tab). Navigate to the General tab. Select Vehicles in the Detectors section.