This demo provides an inference pipeline for person 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-0277, 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 model documentation.
Other demo objectives are:
On startup, the application reads command line parameters and loads the specified networks. The Person Detection network is required, and 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.
If the Person Reidentification Retail network is 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, 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_channelsargument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.
For demo input image or video files, refer to the section Media Files Available for Demos in the Open Model Zoo Demos Overview. The list of models supported by the demo is in
<omz_dir>/demos/crossroad_camera_demo/cpp/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 Inference Engine format (*.xml + *.bin).
An example of using the Model Downloader:
An example of using the Model Converter:
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.
For example, to do inference on a GPU with the OpenVINO™ toolkit pre-trained models, run the following command:
NOTE: The detection network returns as the result a set of detected objects, where each detected object consists of a bounding box and an index of the object's category (person/vehicle/bike). The demo runs Person Attributes Recognition and Person Reidentification networks only for the bounding boxes that has the category "person". Since different detection networks may have different category index corresponding to the category "person", this index may be pointed by the command line parameter
-person_label. Please, note that
- the model
person-vehicle-bike-detection-crossroad-0078returns for persons the category index 1, it is the default value for the demo
- the model
person-vehicle-bike-detection-crossroad-1016returns for persons the category index 2, so for the demo to work correctly, the command line parameter
-person_label 2should be added.
>NOTE: If you provide a single image as an input, the demo processes and renders it quickly, then exits. To continuously visualize inference results on the screen, apply the
loop option, which enforces processing a single image in a loop.
You can save processed results to a Motion JPEG AVI file or separate JPEG or PNG files using the
aviextension, for example:
pngextension, for example:
-o output_%03d.jpg. The actual file names are constructed from the template at runtime by replacing regular expression
%03dwith the frame number, resulting in the following:
output_001.jpg, and so on. To avoid disk space overrun in case of continuous input stream, like camera, you can limit the amount of data stored in the output file(s) with the
limitoption. The default value is 1000. To change it, you can apply the
-limit Noption, where
Nis the number of frames to store.
>NOTE: Windows* systems may not have the Motion JPEG codec installed by default. If this is the case, you can download OpenCV FFMPEG back end using the PowerShell script provided with the OpenVINO ™ install package and located at
<INSTALL_DIR>/opencv/ffmpeg-download.ps1. The script should be run with administrative privileges if OpenVINO ™ is installed in a system protected folder (this is a typical case). Alternatively, you can save results as images.
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: