Configuration

The SFE Stream Processor can be configured in several ways:

Centralized Edge Stream Management via REST API and Video Processing Platform Station
Using GUI Client for Linux and Windows
Locally on device (this section)

To configure the SFE Stream Processor locally you can update settings.yaml file that comes as input of sfe_stream_processor executable.

./sfe_stream_processor /path/to/settings.yaml

The configuration of SFE Stream Processor includes settings of:

Connection
Face detection
Tracking
Face template extraction
Face passive liveness
Face identification
Messaging
Cropping
Full frame
Solvers
License
Logging

SFE Stream Processor configuration can be changed directly by editing the input settings.yaml file. SFE Stream Processor automatically reloads the configuration when there is a change detected in settings.yaml.

An example settings.yaml is documented here

Connection

MQTT (MQ Telemetry Transport) protocol is used for communication between SFE Stream Processor running on edge device (camera or AI box) and Video Processing Platform running on server/cloud.

See below an example configuration:

connection:
  broker_address: 127.0.0.1
  broker_port: 1883
  client_id: camera-01
  topic: edge-stream
  keep_alive: 60
  timeout: 10
  tls_enable: false
  username: mqtt
  password: mqtt

Please find a detailed description of connection parameters below. All connection parameters are related to MQTT.

broker_address

Defines the IP address of a machine where the MQTT broker is running.

Use the IP address of a server/cloud where the Video Processing Platform platform is running if you are using Video Processing Platform for processing the edge stream.
Use 127.0.0.1 if you want to process the MQTT messages locally. You will also need an MQTT broker running on the device.

You can also use a hostname as broker_address if the DNS is enabled on your edge device.

broker_port

TCP/IP port 1883 is reserved with IANA for use with MQTT. TCP/IP port 8883 is also registered, for using MQTT over SSL. Use this port when TLS is enabled.

client_id

Client ID is a string that identifies the MQTT client connected to an MQTT broker. It should be unique for each camera. If you are processing multiple camera streams on one device, it should be unique for each camera stream. When using the Video Processing Platform for processing the MQTT message stream, the same client ID is used during the registration of a new edge stream.

topic

MQTT topic is a UTF-8 string that the broker uses to filter messages for each connected client.

Topic defines the root topic. The topic for a specific camera is then a combination of "topic" and "client_id" - <connection.topic>/<connection.client_id>, for example, edge-stream/camera-01.

For integration with the Video Processing Platform, the root topic should be set to edge-stream. This means the topic for a specific camera is edge-stream/<client_id>

keep_alive

MQTT connection keepalive represents the maximum time in seconds allowed to elapse between MQTT protocol packets sent by the client.

timeout

MQTT connection timeout in seconds. It limits how long the client will wait when trying to open a connection to an MQTT server before timing out.

tls_enable

You can enable TLS to encrypt communication between MQTT clients. The MQTT communication is not encrypted by default and it is highly recommended to use TLS, especially in public networks. TLS is required in case of cloud deployment of the Video Processing Platform.

username and password

MQTT supports username/password authentication to restrict access to an MQTT broker.

The MQTT broker used in the Video Processing Platform requires the following username and password:

  username: mqtt
  password: mqtt

You can set your username and password in the Video Processing Platform RabbitMQ interface running on port 15672, for example:

smartface-platform:15672

Face Detection

This section describes the configuration of face detection.

See below an example configuration:

face_detection:
  detection_threshold: 0.06
  max_detections: 10
  order_by: DetectionConfidence
  min_face_size: null
  max_face_size: null

detection_threshold

The threshold is used to filter low-confidence detections from processing. Detections below this threshold are not considered in tracking. The valid range is <0.0,1.0> The recommended value is 0.06.

max_detections

Defines the maximum number of faces to be detected, tracked and processed per frame.

order_by

Detected faces can be ordered by FaceSize or DetectionConfidence.

min_face_size

Defines minimal face size for detected faces. Faces with face size below this threshold won't be processed and won't be included in the message. The value can be absolute (>1.0, therefore in pixels) or relative to full frame width (<=1.0, therefore face size in pixels /image width in pixels). If not set (null), faces are not filtered by size.

max_face_size

Defines maximal face size for detected faces. Faces with face size above this threshold won't be processed and won't be included in the message. The value can be absolute (>1.0, therefore in pixels) or relative to full frame width (<=1.0, therefore face size in pixels /image width in pixels). If not set (null), faces are not filtered by size.

Tracking

This section describes the configuration of tracking.

See below an example configuration:

tracking:
  threshold: 0.1
  stability: 0.85
  max_frames_lost: 25

threshold

Minimal detection confidence threshold for tracked faces. Faces with detection confidence below this threshold are filtered out by tracking and not propagated to landmarks detection and template extraction. The valid range is <0.0,1.0> The recommended value is 0.1.

stability

Stability is used to associate the tracklets with detections using a Kalman filter. Low stability may cause tracking to jump between detections. High Stability may cause more tracklets to be created. The valid range is <0.0,1.0> The recommended value is 0.85.

max_frames_lost

Defines the maximum number of frames to keep lost faces in tracking. max_frames_lost: 25

Face Template Extraction

This section describes the configuration of template extraction.

See below an example configuration:

face_extraction:
  enable: true

enable

Enable face template extraction. The face template is used for identification.

If disabled (false), extraction is not performed, and face templates are not included in the FrameData. It means the template extraction has to be performed on the server to enable the identification.

Face Passive Liveness

This section describes the configuration of passive liveness evaluation.

See below an example configuration:

face_liveness_passive:
  enable: true
  strategy: OnEachExtractedFace
  conditions:
    - parameter: FaceSize
      lower_threshold: 30.0
    - parameter: FaceRelativeArea
      lower_threshold: 0.009
    - parameter: FaceRelativeAreaInImage
      lower_threshold: 0.9
    - parameter: YawAngle
      lower_threshold: -20.0
      upper_threshold: 20.0
    - parameter: PitchAngle
      lower_threshold: -20.0
      upper_threshold: 20.0
    - parameter: Sharpness
      lower_threshold: 0.6

enable

Enable face passive liveness evaluation.

If disabled (false), liveness evaluation is not performed, and liveness score is not included in the FrameData.

strategy

Defines strategy for liveness detection:

OnEachExtractedFace - If enabled, liveness is computed for each extracted face
OnEachIdentifiedFace - If enabled, liveness is computed for each recognized/identified face

conditions

Conditions defining minimal quality of the input face image for reliable liveness/spoof detections. The recommended thresholds are documented at Face Liveness Detection If the conditions are not met liveness score is not computed.

Face Identification

This section describes the configuration of face identification.

See below an example configuration:

face_identification:
  enable: true
  storage: /root/sfe/records_storage.bin
  candidate_count: 1
  threshold: 0.40

enable

Enable face identification. If disabled (false), identification is not performed, and the identification results are not included in the FrameData.

storage

Defines the path to the directory on your file system to store the data for identification purposes.

candidate_count

Defines the maximum number of candidates returned from identification.

threshold

Defines the matching score threshold for identified candidates. Candidates with a matching score below this threshold are not considered positive matches and are not returned. The valid range is <0.0,1.0> The recommended value is 0.40.

Messaging

SFE Stream Processor sends messages containing face recognition analytics (FrameData) to the MQTT broker configured in the connection section.

See below an example configuration:

messaging:
  enable: true
  format: Protobuf
  strategy: OnNewAndInterval
  interval: 500
  allow_empty_messages: false

enable

Enable or disable sending messages from the SFE Stream Processor.

format

Defines the format of the message serialization. Supported message formats are Json, Yaml and Protobuf.

strategy

Defines the messaging strategy. Currently, the OnNewAndInterval strategy is supported. It means the message is sent when a new face is detected or the interval is expired.

interval

Defines the time interval in milliseconds for sending messages. The message is sent at least each <interval> milliseconds.

allow_empty_messages

By default, the message is sent only when:

a new face is detected or
at least one face is tracked This parameter enables sending empty messages. It is meant for debugging purposes only. An empty message means there are no detected or tracked faces

Crop image

This section describes the configuration of cropping.

See below an example configuration:

crop:
  enable: true
  size_extension: 2
  max_size: 50.0
  image_format: Raw
  image_quality: 0.85

enable

Enable sending crop images of detected faces. Crop images can be used for post-processing or storage purposes on the server/cloud.

size_extension

Defines the size of the crop as an extension of the detection bounding box. The crop will be centered on the detection bounding box and the size will be multiplied by this value. Valid values are 0, 1, 2, 3, 4 and 5. You should use the correct value depending on the post-processing required on the server side, for example:

Template extraction requires size_extension=2
Passive liveness detection requires size extension=5

max_size

Defines the maximum size of the face in the crop area. Once a face is detected and its crop area is calculated using face_size_extension, this value will be used to determine the scale of the crop.

image_format

Specifies the format of the cropped images sent in a message. Supported image formats are Raw, Jpeg and PNG.

image_quality

Image quality for image formats that support compression. The range is from 0.0 for very low quality (high compression ratio) to 1.0 for maximum quality (no compression).

Full frame

This section describes the configuration of full frames.

See below an example configuration:

full_frame:
  enable: false
  image_width: null
  image_height: null
  image_format: Raw
  image_quality: 0.85

enable

Enable sending full-frame images. Please note sending a full-frame image requires a lot of bandwidth. That is why this parameter should be used for debugging purposes only.

image_width

The width of the full-frame image. If not specified (null), the default width is used.

image_height

The height of the full-frame image. If not specified (null), the default height is used.

image_format

Specifies the format of the full-frame image sent in a message. Supported image formats are Raw, Jpeg and PNG.

image_quality

Image quality for image formats that support compression. The range is from 0.0 for very low quality (high compression ratio) to 1.0 for maximum quality (no compression).

Solvers

This section describes the configuration of solvers.

See below an example configuration:

solvers:
  frame_input: 
    solver: /emmc/plugin/Inno/solver/camera-input.ambarella.solver.0.7.3
    parameters: []
  frame_output:
    solver: null
    parameters: []
  face_detection:
    solver: /emmc/plugin/Inno/solver/face_detect_accurate_mask_w1280h720_perf.ambarella.solver.0.7.3
    parameters: []
  face_landmarks:
    solver: /emmc/plugin/Inno/solver/face_landmarks_0.25_acc.ambarella.solver.0.7.3
    parameters: []
  face_extraction:
    solver: /emmc/plugin/Inno/solver/face_template_extract_accurate_mask_perf.ambarella.solver.0.7.3
    parameters: []
  face_liveness_passive:
    solver: null
    parameters: []

For each solver, you can also define solver-specific parameters as an array of name and value pairs. Support solver parameters are documented in SFE Solver.

To apply changes in solver configuration it is required to restart the SFE Stream Processor.

frame_input

Defines the relative or full path to frame input solver.

The frame input solver is responsible for loading the frame data from various video sources.

See also Input solvers for more information on how to configure the input solver properly.

frame_output

For debugging purposes only.

Defines the relative or full path to the frame output solver. Can be used for the output stream annotations.

face_detection

Defines the relative or full path to face detection solver.

face_landmarks

Defines the relative or full path to face landmarks solver.

face_extraction

Defines the relative or full path to face template extraction solver.

face_liveness_passive

Defines the relative or full path to face passive liveness solver.

License

In this section, you can set the license to be used for the initialization of the SFE Stream Processor

See below an example configuration:

license:
  data: ewogICAgInZlcnNpb24iOiAiMi4xIiwKICAgI...

To apply changes in License data it is required to restart the SFE Stream Processor.

data

Base64 encoded license data.

The default value is null, which means the license file from the file system or ILICENSE_DATA environment variable is used. For more information, see also Licensing documentation.

Log

This section describes the configuration of logging.

See below an example configuration:

log:
  level: Info
  path: /emmc/plugin/Inno/log/sfe_stream_processor.log

To apply changes in logging configuration it is required to restart the SFE Stream Processor.

level

Specifies the log level. Supported values are:

Off - logging is turned off
Error - only error messages are logged
Warn - additionally warning messages are logged
Info - additionally info messages are logged
Debug - additionally debug messages are logged
Trace - additionally trace messages are logged.

The default log level is Info

path

Defines the path to a log file. When no path is set, standard error (stderr) is used.

By default, no log file path is set.

SFE Stream processor configuration example

# MQTT Connection
connection:
  # Address of the MQTT broker server
  broker_address: 10.11.82.88
  # Port of the MQTT broker server (default: 1883)
  broker_port: 1883
  # Client ID of the MQTT client, when empty a random UUID will be generated on startup
  client_id: camera-01
  # MQTT root topic path
  topic: edge-stream
  # Connection keep alive in seconds
  keep_alive: 60
  # Connection timeout in seconds
  timeout: 10
  # Enable TLS
  tls_enable: false
  # Username for authentication
  username: mqtt
  # Password for authentication
  password: mqtt

# Face detection
face_detection:
  # The threshold is used to filter low-confidence detections from processing. Detections below this threshold are not considered in tracking.
  detection_threshold: 0.06
  # Limit the number of faces to be detected
  max_detections: 10
  # Detected faces are order by (FaceSize, DetectionConfidence)
  order_by: DetectionConfidence
  # Minimal detectable face size
  min_face_size: null
  # Maximal detectable face size
  max_face_size: null


# Face tracking
tracking:
  # Minimal detection confidence threshold for tracked faces. Faces with detection confidence below this threshold are filtered out by tracking and not propagated to landmarks detection and template extraction.
  threshold: 0.1
  # Stability of tracking
  #   low stability may cause tracking to jump between detections
  #   high Stability may cause more tracklets to be created
  stability: 0.85
  # Maximum number of frames a face can be lost before it is removed from the tracking list
  max_frames_lost: 25

# Face extraction
face_extraction:
  # Enable face extraction, if disabled identification will not work
  enable: true

# Face passive liveness:
face_liveness_passive:
  # Enable face passive liveness calculation, if disabled, liveness score is not calculated
  enable: false
  # Face liveness strategy
  strategy: OnEachExtractedFace
  # Liveness conditions for reliable liveness detection
  conditions:
    # Minimal face size
    - parameter: FaceSize
      lower_threshold: 30.0
    # Minimal face relative area
    - parameter: FaceRelativeArea
      lower_threshold: 0.009
    # Minimal face relative area in image
    - parameter: FaceRelativeAreaInImage
      lower_threshold: 0.9
    # Yaw angle range
    - parameter: YawAngle
      lower_threshold: -20.0
      upper_threshold: 20.0
    # Pitch angle range
    - parameter: PitchAngle
      lower_threshold: -20.0
      upper_threshold: 20.0
    # Minimal sharpness 
    - parameter: Sharpness
      lower_threshold: 0.6

# Face identification
face_identification:
  # Enable face identification
  enable: true
  # Path to the face database
  storage: /emmc/plugin/Inno/records_storage.bin
  # Maximum number of candidates to be returned by the identification
  candidate_count: 10
  # Threshold for face identification, identification confidence must be higher than this value
  threshold: 0.40

# Message strategy and format
messaging:
  # Enable message publishing
  enable: true
  # Message format to be used
  #   Json: Json format
  #   Protobuf: Protobuf format
  #   Yaml: Yaml format
  format: Protobuf
  # Publishing strategy:
  #   OnNewAndInterval: Publishes a message when a new face is detected and every interval
  strategy: OnNewAndInterval
  # Publishing interval in milliseconds between messages
  interval: 250
  # Publish a message even when no faces are detected
  allow_empty_messages: false

# Include face image in the message
crop:
  # Enable face image in message
  enable: true
  # Border around the face in face width units
  size_extension: 2
  # Max width of the face image in the message in pixels
  max_size: 50.0
  # Image format of the face image in the message
  #   Jpeg: Jpeg format
  #   Png: Png format
  #   Raw: Raw BGR format
  image_format: Raw
  # Defines image quality in case of image format with compression support
  image_quality: 0.85


# Include full input frame in the message
full_frame:
  # Enable full frame in message
  enable: false
  # Width of the full frame in the message, if null the original frame width will be used
  image_width: null
  # Height of the full frame in the message, if null the original frame height will be used
  image_height: null
  # Image format of the full frame in the message
  #   Jpeg: Jpeg format
  #   Png: Png format
  #   Raw: Raw BGR format
  image_format: Raw
  # Defines image quality in case of image format with compression support
  image_quality: 0.85


# Solvers to be used
solvers:
  # Solver used to acquire the input frame
  frame_input: 
    solver: ./solver/gstreamer_input.cpu.solver
    parameters:
      - name: gst_pipeline
        value: "rtspsrc location=rtsp://root:Innovatrics1@192.168.17.40:554/axis-media/media.amp latency=30 ! rtph264depay ! h264parse ! openh264dec ! videorate ! videoconvert ! videoscale ! video/x-raw,format=BGR,framerate=10/1,width=1280,height=720"
      - name: gst_width
        value: 1280
      - name: gst_height
        value: 720
  # Solver used to output the frame for preview and debugging
  frame_output:
    solver: null
    parameters: []
  # Solver used to detect faces
  face_detection:
    solver: ./solver/face_detect_accurate_mask.onnxrt.solver
    parameters:
    - name: runtime_provider
      value: cpu
    - name: intra_threads
      value: '6'
    - name: inter_threads
      value: '1'
  # Solver used to detect face landmarks
  face_landmarks:
    solver: ./solver/face_landmarks_0.25.onnxrt.solver
    parameters:
    - name: runtime_provider
      value: cpu
    - name: intra_threads
      value: '6'
    - name: inter_threads
      value: '1'
  # Solver used to extract face templates
  face_extraction:
    solver: ./solver/face_template_extract_accurate_mask.onnxrt.solver
    parameters:
    - name: runtime_provider
      value: cpu
    - name: intra_threads
      value: '6'
    - name: inter_threads
      value: '1'
  # Solver used to calculate passive liveness score
  face_liveness_passive:
    solver: ./solver/face_liveness_passive_distant.onnxrt.solver
    parameters:
    - name: runtime_provider
      value: cpu
    - name: intra_threads
      value: '6'
    - name: inter_threads
      value: '1'

license:
  data: null
log:
  level: Info
  path: /emmc/plugin/Inno/log/sfe_stream_processor.log

Input solvers

The frame_input solver loads frames from a video source. The following input solvers are available (availability varies by platform).

Camera input solver

Opens a camera device with a given index and outputs the image as an output tensor.

Parameter	Type	Description	Default
`camera_index`	u32	Index of the Linux camera device	0
`camera_width`	u32	Width of the camera resolution	640
`camera_height`	u32	Height of the camera resolution	480
`camera_fps`	u32	Camera framerate	5
`camera_format`	string	Frame format: `MJPEG`, `YUYV`, `GRAY`, `RAWRGB`, `NV12`	YUYV

You can list available cameras with gst-device-monitor-1.0.

GStreamer input solver

Runs a GStreamer pipeline, consumes its output, and creates a tensor. A GStreamer pipeline can obtain BGR frames from a USB camera, a video file, or an RTSP stream.

Default pipeline:

v4l2src device={gst_video_device} ! video/x-raw,width={gst_width},height={gst_height},framerate=10/1 ! videoconvert ! video/x-raw,format=BGR | appsink name={app_sink} drop=True max-buffers=1 emit-signals=True async=false sync=false

Parameter	Type	Description
`gst_pipeline`	string	Complete GStreamer pipeline that overrides every other parameter
`gst_width`	u32	Width of the camera resolution
`gst_height`	u32	Height of the camera resolution
`gst_app_sink_name`	string	App sink name
`gst_video_device`	string	Linux video device, e.g. `/dev/video1`

Rules for a custom GStreamer pipeline:

Do not specify any *sink (appsink, autovideosink, …).
Always make sure the pipeline outputs a video/x-raw,format=BGR frame.
Check that the width/height properties are set correctly.
Check that your detector model accepts the same input shape the pipeline provides.

See also GStreamer pipelines for how to configure the pipeline properly.

Image input solver

Simulates a camera stream by reading images from a folder (default: the current directory). Images are sorted by name before streaming. Intended for testing and development.

Parameter	Type	Description
`path`	string	Path to the folder of images to stream

GStreamer pipelines

Generic rules for using GStreamer pipelines

In this article, there are multiple examples of the GStreamer pipeline and they can be used in 2 different ways.

Using gst-launch-1.0

The gst-launch-1.0 tool should be used for pipeline validation. You need to visualize the pipeline output first, then you can pass it to the sfe_stream_processor. If you’re trying to run a Gstreamer pipeline from this article with gst-launch-1.0, you should modify the example pipeline as follows:

$ gst-launch-1.0 <here goes example pipeline of your choice> \
! videoconvert ! xvimagesink

Parts of the modified pipeline:

first, you prepend gst-launch-1.0
then you place your pipeline right after the gst-launch-1.0
videoconvert element goes right after the example pipeline (converts frames to the correct format for pipeline sink)
the last element is a pipeline sink (recommended is xvimagesink)

Using sfe_stream_processor

In the SFE stream processor, you have to write down a GStreamer pipeline in settings.yaml file (this is the file the sfe_stream_processor takes as its input). The YAML file contains lots of different fields but here, we focus only on frame_input section:

frame_input:
    solver: ./solver/gstreamer_input.cpu.solver
    parameters:
      - name: gst_pipeline
        value: "here goes example pipeline"
	    - name: gst_width
        value: <frame_width>
      - name: gst_height
        value: <frame_height>

If we consider a specific example:

frame_input:
    solver: ./solver/gstreamer_input.cpu.solver
    parameters:
      - name: gst_pipeline
        value: "rtspsrc location=rtsp://root:Innovatrics1@192.168.17.40:554/axis-media/media.amp latency=30 ! rtph264depay ! h264parse ! openh264dec ! videorate ! videoconvert ! videoscale ! video/x-raw,format=BGR,framerate=10/1,width=1280,height=720"
      - name: gst_width
        value: 1280
      - name: gst_height
        value: 720

See also GStreamer input solver for more information and parameters.

There are a few rules to follow while designing your pipeline:

the pipeline MUST convert the video source to BGR format
width/height properties MUST specify frame resolution going out of your pipeline
sink elements MUST NOT be specified
in most cases, the last part of your pipeline should contain videoconvert ! video/x-raw,format=BGR

Example pipelines

Pipeline using MP4 video file

filesrc location=test.mp4 \
! qtdemux ! h264parse ! avdec_h264 \
! videorate drop-only=true \
! videoconvert ! video/x-raw,format=BGR,framerate=10/1

Pipeline using MP4 video file and Intel VA-API elements

filesrc location=test.mp4 \
! qtdemux ! vaapidecodebin \
! queue leaky=no max-size-buffers=5 max-size-bytes=0 max-size-time=0 \
! vaapipostproc ! videorate \
! video/x-raw,framerate=10/1,width=1280,height=720 \
! queue leaky=no max-size-buffers=5 max-size-bytes=0 max-size-time=0 \
! videoconvert ! video/x-raw,format=BGR

Pipeline using RTSP stream

rtspsrc location=rtsp://aa.bb.cc.dd/strm latency=1 \
! queue ! rtph264depay ! h264parse ! avdec_h264 \
! videorate ! videoconvert ! videoscale \
! video/x-raw,format=BGR,width=1280,height=720,framerate=10/1

Pipeline using USB camera

v4l2src device=/dev/video0 ! video/x-raw,framerate=10/1,width=1280,height=720 \
! videoconvert ! video/x-raw,format=BGR

Note: You may be interested in using another GStreamer tool gst-device-monitor-1.0 that lists all input devices. Simplified example output of this tool run on a laptop with Webcam:

Device found:

	name  : Integrated_Webcam_HD: Integrate
	class : Video/Source
	caps  : video/x-raw, format=YUY2, width=640, height=480, pixel-aspect-ratio=1/1, framerate=30/1
			...
	        image/jpeg, width=1280, height=720, pixel-aspect-ratio=1/1, framerate=30/1
			...
	properties:
		...
		device.subsystem = video4linux
		device.product.name = Integrated_Webcam_HD: Integrate
		...
		device.path = /dev/video0
		...
	gst-launch-1.0 v4l2src ! ...

Connection​

broker_address​

broker_port​

client_id​

topic​

keep_alive​

timeout​

tls_enable​

username and password​

Face Detection​

detection_threshold​

max_detections​

order_by​

min_face_size​

max_face_size​

Tracking​

threshold​

stability​

max_frames_lost​

Face Template Extraction​

enable​

Face Passive Liveness​

enable​

strategy​

conditions​

Face Identification​

enable​

storage​

candidate_count​

threshold​

Messaging​

enable​

format​

strategy​

interval​

allow_empty_messages​

Crop image​

enable​

size_extension​

max_size​

image_format​

image_quality​

Full frame​

enable​

image_width​

image_height​

image_format​

image_quality​

Solvers​

frame_input​

frame_output​

face_detection​

face_landmarks​

face_extraction​

face_liveness_passive​

License​

data​

Log​

level​

path​

SFE Stream processor configuration example​

Input solvers​

Camera input solver​

GStreamer input solver​

Image input solver​

GStreamer pipelines​

Using gst-launch-1.0​

Using sfe_stream_processor​

Example pipelines​

Pipeline using MP4 video file​

Pipeline using MP4 video file and Intel VA-API elements​

Pipeline using RTSP stream​

Pipeline using USB camera​

Connection

broker_address

broker_port

client_id

topic

keep_alive

timeout

tls_enable

username and password

Face Detection

detection_threshold

max_detections

order_by

min_face_size

max_face_size

Tracking

threshold

stability

max_frames_lost

Face Template Extraction

enable

Face Passive Liveness

enable

strategy

conditions

Face Identification

enable

storage

candidate_count

threshold

Messaging

enable

format

strategy

interval

allow_empty_messages

Crop image

enable

size_extension

max_size

image_format

image_quality

Full frame

enable

image_width

image_height

image_format

image_quality

Solvers

frame_input

frame_output

face_detection

face_landmarks

face_extraction

face_liveness_passive

License

data

Log

level

path

SFE Stream processor configuration example

Input solvers

Camera input solver

GStreamer input solver

Image input solver

GStreamer pipelines

Using gst-launch-1.0

Using sfe_stream_processor

Example pipelines

Pipeline using MP4 video file

Pipeline using MP4 video file and Intel VA-API elements

Pipeline using RTSP stream

Pipeline using USB camera