Is possible to collect/filter useful data via unsupervised learning approach to save manual checking time?
Cluster the Yoga crawler mess data to collect grouping data
The Seated Yoga Poses group together in the upper middle part of the picture.
TODO:
Try to use pose-estimation instead of ImageNet pretrained model to extract people embeddings
Filter out the outlier(fused with black image) with larger cosine distance
TODO:
Try to use Mask-RCNN to crop people to remove background noise
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In Tensorflow2.x
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Collect the mess data
$ python main.py --data_path your_crawler_mess_poses
Maybe we can try to use segmentation to remove people background noise and use pose-estimation to extract good embeddings to get better results in the future.