Create custom people detector for automated labelling on own specific dataset

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Candice Earley
Candice Earley on 1 Oct 2021
Commented: Mahesh Taparia on 8 Oct 2021
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.

Answers (2)

Mahesh Taparia
Mahesh Taparia on 4 Oct 2021
Hi
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!

Candice Earley
Candice Earley on 6 Oct 2021
I am having trouble actually importing those various detectors into ImageLabeler. I have tried to run the matlab examples for creating a rcnnmask but the examples gives errors such as "unrecognized variable or function segmentObjects" and tried loading a pretrained RCNN gives "variable 'net' orginally saved as maskrcnn cannot be instantiated as an object and will be read as an unint32".
Could you possibly assist? Where can I find the necessary files to import those detectors in ImageLabeler? (since can't be .mat files and should be .m I assume)
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