This demo showcases Pedestrian Tracking scenario: it reads frames from an input video sequence, detects pedestrians in the frames, and builds trajectories of movement of the pedestrians in a frame-by-frame manner.
On startup, the application reads command line parameters and loads the specified networks.
Upon getting a frame from the input video sequence (either a video file or a folder with images), the app performs inference of the pedestrian detector network.
After that, the bounding boxes describing the detected pedestrians are passed to the instance of the tracker class that matches the appearance of the pedestrians with the known (already tracked) persons. In obvious cases (when pixel-to-pixel similarity of a detected pedestrian is sufficiently close to the latest pedestrian image from one of the known tracks), the match is made without inference of the reidentification network. In more complicated cases, the demo uses the reidentification network to make a decision if a detected pedestrian is the next position of a known person or the first position of a new tracked person.
After that, the application displays the tracks and the latest detections on the screen and goes to the next frame.
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/pedestrian_tracker_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:
For example, to run the application with the OpenVINO™ toolkit pre-trained models with inferencing pedestrian detector on a GPU and pedestrian reidentification on a CPU, run the following command:
>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, curves (for trajectories displaying), and text.