High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection (center and scale prediction)

April 2019

tl;dr: an anchor free method to directly predict the center and scale of pedestrian (single class) bounding boxes. It was heavily influenced by CornerNet but reformulate the object detection task to get rid of the data association problem.

Overall impression

The architecture is surprisingly simple. Previous methods require tedious config in windows or anchors. The method is less prone to occlusion of bbox as it predicts the center of amodal bbox directly. Also it only focuses on the binary classification problem. How to extend this to general object detection remains to be explored.

Key ideas

Technical details