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. 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 model documentation.
On the start-up, 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
--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.
Running the application with the
-h option yields the following usage 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 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:
The demo uses OpenCV to display the resulting frame with detections rendered as bounding boxes, curves (for trajectories displaying), and text.
NOTE: On VPU devices (Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs) this demo has been tested on the following Model Downloader available topologies: >*
person-reidentification-retail-0031Other models may produce unexpected results on these devices.