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Vehicle model recognition performs best when vehicles are moving towards the camera, and the front of the vehicle is visible in the video.
To obtain training images, you can run vehicle model identification on a sample video and output the result images to the image encoder. The vehicle model analysis engine requires as input either the
ResultWithSource track from a number plate analysis task, or the
ResultWithSource track from a text detection task. Before you output images using the image encoder, configure a
Crop transform task so that the images are cropped to the region identified by the vehicle model analysis engine.
For example, you could use the following configuration:
[Ingest] IngestEngine=libav [libav] Type=libav [Analysis] AnalysisEngine0=ANPR AnalysisEngine1=VehicleModel [ANPR] Type=numberplate Location=GB [VehicleModel] Type=vehiclemodel Input=ANPR.DataWithSource [Transform] TransformEngine0=Crop [Crop] Type=Crop Input=VehicleModel.ResultWithSource [Encoding] EncodingEngine0=VehicleImages [VehicleImages] Type=ImageEncoder ImageInput=Crop.Output OutputPath=./training/image-%segment.sequence%.png