Vehicle and Pedestrian Tracking Sample (gst-launch command line)

This sample demonstrates gvatrack element and object tracking capabilities on example of person and vehicle tracking. Object tracking increases performance by running inference on object detection and classification models less frequently (not every frame).

How It Works

The sample utilizes GStreamer command-line tool gst-launch-1.0 which can build and run GStreamer pipeline described in a string format. The string contains a list of GStreamer elements separated by exclamation mark !, each element may have properties specified in the format property=value.

The gvadetect element sets inference-interval property to 10 frames in this sample, so inference on object detection model executed every 10th frame.

The gvatrack element inserted into pipeline after gvadetect to track all objects on remaining 9 frames until object detection executed again.

The gvaclassify element sets reclassify-interval property to 10, so inference on object classification model executed every 10th frames. gvaclassify uses unique object ID assigned by gvatrack to each object for copying classification results on remaining 9 frames from last frame inference was executed.

Overall this sample builds GStreamer pipeline of the following elements

  • filesrc or urisourcebin or v4l2src for input from file/URL/web-camera
  • decodebin for video decoding
  • videoconvert for converting video frame into different color formats
  • gvadetect for person and vehicle detection based on OpenVINO™ Toolkit Inference Engine
  • gvatrack for tracking objects
  • gvaclassify inserted into pipeline twice for person and vehicle classification
  • gvawatermark for bounding boxes and labels visualization
  • fpsdisplaysink for rendering output video into screen

    NOTE: sync=false property in fpsdisplaysink element disables real-time synchronization so pipeline runs as fast as possible

    Models

The sample uses by default the following pre-trained models from OpenVINO™ Toolkit Open Model Zoo

  • person-vehicle-bike-detection-crossroad-0078 is primary detection network for detecting persons, vehicles and bikes
  • person-attributes-recognition-crossroad-0230 classifies person attributes
  • vehicle-attributes-recognition-barrier-0039 classifies vehicle attributes

NOTE: Before running samples (including this one), run script download_models.sh once (the script located in samples top folder) to download all models required for this and other samples.

The sample contains model_proc subfolder with .json files for each model with description of model input/output formats and post-processing rules for classification models.

Running

./vehicle_pedestrian_tracking.sh [INPUT_VIDEO] [DETECTION_INTERVAL] [DEVICE] [SINK_ELEMENT] [TRACKING_TYPE]

The sample takes four command-line parameters:

  1. [INPUT_VIDEO] to specify input video. The input could be
    • video file path
    • web camera device (ex. /dev/video0)
    • URL of RTSP camera (URL starts with rtsp://) or other streaming source (ex http://) If parameter is not specified, the sample by default streams video example from HTTPS link (utilizing urisourcebin element) so requires internet conection.
  2. [DETECTION_INTERVAL] to specify interval between inference requests. An interval of N performs inference on every Nth frame. Default value is 10
  3. [DEVICE] to specify device for detection and classification. Please refer to OpenVINO™ toolkit documentation for supported devices. https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_supported_plugins_Supported_Devices.html You can find what devices are supported on your system by running following OpenVINO™ toolkit sample: https://docs.openvinotoolkit.org/latest/openvino_inference_engine_ie_bridges_python_sample_hello_query_device_README.html
  4. [SINK_ELEMENT] to choose between render mode and fps throughput mode:
    • display - render (default)
    • fps - FPS only
  5. [TRACKING_TYPE] to specify tracking type. Tracking types available are short-term(default), short-term-imageless, zero-term, zero-term-imageless. For more information on tracking types and their difference, please turn to https://github.com/openvinotoolkit/dlstreamer_gst/wiki/Object-tracking.

Sample Output

The sample

  • prints gst-launch command line into console
  • starts the command and visualizes video with bounding boxes around detected objects and text with classification results (color, type and others) for each detected object or prints out fps if you set SINK_ELEMENT = fps

See also