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:
person-vehicle-bike-detection-crossroad-0078, which is a primary detection network for finding the persons (and other objects if needed)
person-attributes-recognition-crossroad-0230, which is executed on top of the results from the first network and reports person attributes like gender, has hat, has long-sleeved clothes
person-reidentification-retail-0079, which is executed on top of the results from the first network and prints a vector of features for each detected person. This vector is used to conclude if it is already detected person or not.
For more information about the pre-trained models, refer to the https://github.com/opencv/open_model_zoo/blob/master/intel_models/index.md "Open Model Zoo" repository on GitHub*.
Other demo objectives are:
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_channelsargument 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:
Running the application with an empty list of options yields the usage message given above and an error message.
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:
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: