Human segmentation is a computer vision task that isolates human figures from complex backgrounds, ranging from easy, centered figures to occluded figures in unfavorable environments, including poor lighting. It is an active and challenging research field with diverse applications and approaches toward perfect, or near-perfect, human segmentation. Some of these approaches are generally applied across different domains, and as solutions for segmenting objects other than humans. Many of them achieve impressive results on their object of concern.
In this experiments, I took ideas from existing literature on how segmentation task was approached and apply it to segmenting human figures in images. The algorithms and implementations may not exactly match what's suggested in the literature; they're inspired by it.
- LIP 2000 images from Human Parsing Dataset
- COCO 2000 images of person-class subset of COCO 2017 validation set
- Penn-Fudan 170 images from Penn-Fudan Pedestrian Dataset
- MADS 1192 images from Martial Arts, Dancing and Sports dataset
All datasets contain RGB images with pixel-level binary human segmentation masks.
- YOLO26 + UNET - Live Demo of model's performance
See results for sample results on images and comparison tables across models and datasets.