This demo showcases Object Detection with CenterNet. The task is to identify objects as axis-aligned boxes in an image. CenterNet models an object as a single point - the center point of its bounding box and uses keypoint estimation to find center points and regresses to object size. The same approach is used to estimate 3D bounding box and pose.
On the start-up, the application reads command-line parameters and loads a network to the Inference Engine. Upon getting a frame from the OpenCV VideoCapture, it performs inference and displays the results.
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.
The demo uses OpenCV to display the resulting frame with detections (rendered as bounding boxes and labels, if provided) and reports performance in the following format: summary inference FPS.