Create custom people detector for automated labelling on own specific dataset
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I have applied the built-in ACFpeopleDetector for automated labelling on my own dataset of people in a rail station (not upright still images) and it is not very accurate. I have also tried to create and train a custom ACFobjectDetector (307 training images for 3075 dataset) but is not an improvement. Is there a way to pre-process (like instance segmentation) a dataset of people in a rail station to make the labelling more accurate? The dataset is people walking through a specific area at an overhead angle and thus the frames are not perfectly still images of people and many times with people behind one another.
Mahesh Taparia on 4 Oct 2021
In order to label the objects, you can use image labeler app with custom algorithm option.The custom algorithm can be YOLOv3/ FasterRCNN/ any deep learning network pretrained on COCO dataset. For more information on custom algorithm approach, you can refer to this documentation and this example. Try to use YOLO3,it may work in your case.
Hope it will help!