  Object detection has recently experienced substantial progress. Yet, the
widely adopted horizontal bounding box representation is not appropriate for
ubiquitous oriented objects such as objects in aerial images and scene texts.
In this paper, we propose a simple yet effective framework to detect
multi-oriented objects. Instead of directly regressing the four vertices, we
glide the vertex of the horizontal bounding box on each corresponding side to
accurately describe a multi-oriented object. Specifically, We regress four
length ratios characterizing the relative gliding offset on each corresponding
side. This may facilitate the offset learning and avoid the confusion issue of
sequential label points for oriented objects. To further remedy the confusion
issue for nearly horizontal objects, we also introduce an obliquity factor
based on area ratio between the object and its horizontal bounding box, guiding
the selection of horizontal or oriented detection for each object. We add these
five extra target variables to the regression head of faster R-CNN, which
requires ignorable extra computation time. Extensive experimental results
demonstrate that without bells and whistles, the proposed method achieves
superior performances on multiple multi-oriented object detection benchmarks
including object detection in aerial images, scene text detection, pedestrian
detection in fisheye images.
