Smartlab Python* Demo

This is the demo application with smartlab action recognition and smartlab object detection algorithms. This demo takes multi-view video inputs to identify actions and objects, then evaluates scores of current state. Action recognition architecture uses two encoders for front-view and top-view respectively, and a single decoder. Object detection uses two models for each view to detect large and small objects, respectively. The following pre-trained models are delivered with the product:

  • i3d-rgb-tf + smartlab-sequence-modelling-0001, which are other models for identifying actions 2 actions of smartlab (adjust_rider, put_take).

  • smartlab-object-detection-0001 + smartlab-object-detection-0002 + smartlab-object-detection-0003 + smartlab-object-detection-0004, which are models for detecting smartlab objects (10 objects). They detect balance, weights, tweezers, box, battery, tray, ruler, rider, scale, hand.

How It works

The demo pipeline consists of several steps:

  • Decode reads frames from the input videos

  • Detector detects smartlab objects (balance, weights, tweezers, box, battery, tray, ruler, rider scale, hand)

  • Segmentor segments video frames based on action of the frame

  • Evaluator calculates scores of the current state

  • Display displays detected objects, recognized action, calculated scores on the current frame


By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with the --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Embedding Preprocessing Computation.

Preparing to Run

For demo input image or video files, you need to provide smartlab videos ( The list of models supported by the demo is in <omz_dir>/demos/smartlab_demo/python/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO format (*.xml + *.bin).

An example of using the Model Downloader:

omz_downloader --list models.lst

Supported Models

  • smartlab-object-detection-0001

  • smartlab-object-detection-0002

  • smartlab-object-detection-0003

  • smartlab-object-detection-0004

  • smartlab-sequence-modelling-0001

  • i3d-rgb-tf


Refer to the tables Intel’s Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.


Running the demo with -h shows this help message:

usage: [-h] [-d DEVICE] -tv TOPVIEW -fv FRONTVIEW -m_ta M_TOPALL -m_tm M_TOPMOVE -m_fa M_FRONTALL
                        -m_fm M_FRONTMOVE -m_en M_ENCODER -m_de M_DECODER

  -h, --help            Show this help message and exit.
  -d DEVICE, --device DEVICE
                        Optional. Specify the target to infer on CPU or GPU.
  -tv TOPVIEW, --topview TOPVIEW
                        Required. Topview stream to be processed. The input must be a single image, a folder of images,
                        video file or camera id.
  -fv FRONTVIEW, --frontview FRONTVIEW
                        Required. FrontView to be processed. The input must be a single image, a folder of images,
                        video file or camera id.
  -m_ta M_TOPALL, --m_topall M_TOPALL
                        Required. Path to topview all class model.
  -m_tm M_TOPMOVE, --m_topmove M_TOPMOVE
                        Required. Path to topview moving class model.
  -m_fa M_FRONTALL, --m_frontall M_FRONTALL
                        Required. Path to frontview all class model.
  -m_fm M_FRONTMOVE, --m_frontmove M_FRONTMOVE
                        Required. Path to frontview moving class model.
  -m_en M_ENCODER, --m_encoder M_ENCODER
                        Required. Path to encoder model.
  -m_de M_DECODER, --m_decoder M_DECODER
                        Required. Path to decoder model.

For example, to run the demo, please provide the model paths and two input streams:

    -tv ./stream_1_top.mp4
    -fv ./stream_1_high.mp4
    -m_ta "./intel/smartlab-object-detection-0001/FP32/smartlab-object-detection-0001.xml"
    -m_tm "./intel/smartlab-object-detection-0002/FP32/smartlab-object-detection-0002.xml"
    -m_fa "./intel/smartlab-object-detection-0003/FP32/smartlab-object-detection-0003.xml"
    -m_fm "./intel/smartlab-object-detection-0004/FP32/smartlab-object-detection-0004.xml"
    -m_en "./public/i3d-rgb-tf/FP32/i3d-rgb-tf.xml"
    -m_de "./intel/smartlab-sequence-modelling-0001/FP32/smartlab-sequence-modelling-0001.xml"

Demo Output

The application uses OpenCV to display the real-time object detection, action recognition results and evaluation results.