ACPNet:Anchor-Center Based Person Network for Human Pose Estimation and Instance Segmentation
Yang Bai, Weiqiang Wang · 2019
We present an effective approach to tackle the multi-person pose estimation and person instance segmentation jointly. The approach based on Mask R-CNN uses a set of well-designed labels, called anchor-center based label, to learn keypoints localization in complex and crowded multi-person scenes. Combining the annotation of bounding boxes, we adopt a regression method to predict heatmaps and 2D-offset vector fields based on anchor-center for each keypoint type. Instead of using a person-detector, we use the person proposal network to predict anchors' locations in images. To generate high-quality segmentation mask, we use the ResNet-FPN with the deformable convolutions to model geometric transformations for non-rigid objects. Our method can efficiently and effectively deal with human pose estimation and instance segmentation tasks in a flexible end-to-end manner. Without bells and whistles, our method achieves a comparable result on the COCO keypoints task and the state-of-the-art accuracy on the COCO person instance segmentation task.