Crossroad Camera C++ Demo

This demo provides an inference pipeline for persons' detection, recognition and reidentification. The demo uses Person Detection network followed by the Person Attributes Recognition and Person Reidentification Retail networks applied on top of the detection results. You can use a set of the following pre-trained models with the demo:

For more information about the pre-trained models, refer to the "Open Model Zoo" repository on GitHub*.

Other demo objectives are:

How It Works

On the start-up, the application reads command line parameters and loads the specified networks. The Person Detection network is required, the other two are optional.

Upon getting a frame from the OpenCV VideoCapture, the application performs inference of Person Detection network, then performs another two inferences of Person Attributes Recognition and Person Reidentification Retail networks if they were specified in the command line, and displays the results.

In case of a Person Reidentification Retail network specified, the resulting vector is generated for each detected person. This vector is compared one-by-one with all previously detected persons vectors using cosine similarity algorithm. If comparison result is greater than the specified (or default) threshold value, it is concluded that the person was already detected and a known REID value is assigned. Otherwise, the vector is added to a global list, and new REID value is assigned.

NOTE: By default, Inference Engine samples and 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 sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument specified. For more information about the argument, refer to When to Specify Input Shapes section of Converting a Model Using General Conversion Parameters.


Running the application with the -h option yields the following usage message:

./crossroad_camera_demo -h
API version ............ <version>
Build .................. <number>
crossroad_camera_demo [OPTION]
-h Print a usage message.
-i "<path>" Required. Path to a video or image file. Default value is "cam" to work with camera.
-m "<path>" Required. Path to the Person/Vehicle/Bike Detection Crossroad model (.xml) file.
-m_pa "<path>" Optional. Path to the Person Attributes Recognition Crossroad model (.xml) file.
-m_reid "<path>" Optional. Path to the Person Reidentification Retail model (.xml) file.
-l "<absolute_path>" Optional. For CPU custom layers, if any. Absolute path to a shared library with the kernels impl.
-c "<absolute_path>" Optional. For GPU custom kernels, if any. Absolute path to the xml file with the kernels desc.
-d "<device>" Optional. Specify the target device for Person/Vehicle/Bike Detection (CPU, GPU, FPGA, HDDL, MYRIAD, or HETERO).
-d_pa "<device>" Optional. Specify the target device for Person Attributes Recognition (CPU, GPU, FPGA, HDDL, MYRIAD, or HETERO).
-d_reid "<device>" Optional. Specify the target device for Person Reidentification Retail (CPU, GPU, FPGA, HDDL, MYRIAD, or HETERO).
-pc Optional. Enables per-layer performance statistics.
-r Optional. Output Inference results as raw values.
-t Optional. Probability threshold for person/vehicle/bike crossroad detections.
-t_reid Optional. Cosine similarity threshold between two vectors for person reidentification.
-no_show Optional. No show processed video.
-auto_resize Optional. Enables resizable input with support of ROI crop & auto resize.

Running the application with an empty list of options yields the usage message given above and an error message.

To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader or go to

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

For example, to do inference on a GPU with the OpenVINO™ toolkit pre-trained models, run the following command:

./crossroad_camera_demo -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/person-vehicle-bike-detection-crossroad-0078.xml -m_pa <path_to_model>/person-attributes-recognition-crossroad-0230.xml -m_reid <path_to_model>/person-reidentification-retail-0079.xml -d GPU

Demo Output

The demo uses OpenCV to display the resulting frame with detections rendered as bounding boxes and text. In the default mode, the demo reports Person Detection time - inference time for the Person/Vehicle/Bike Detection network.

If Person Attributes Recognition or Person Reidentification Retail are enabled, the additional info below is reported also:

See Also