  With the renaissance of neural networks, object detection has slowly shifted
from a bottom-up recognition problem to a top-down approach. Best in class
algorithms enumerate a near-complete list of objects and classify each into
object/not object. In this paper, we show that strong performance can still be
achieved using a bottom-up approach for vision-based object recognition tasks
and achieve competitive video game play. We propose PuckNet, which is used to
detect four extreme points (top, left, bottom, and right-most points) and one
center point of objects using a fully convolutional neural network. Object
detection is then a purely keypoint-based appearance estimation problem,
without implicit feature learning or region classification. The method proposed
herein performs on-par with the best in class region-based detection methods,
with a bounding box AP of 36.4% on COCO test-dev. In addition, the extreme
points estimated directly resolve into a rectangular object mask, with a COCO
Mask AP of 17.6%, outperforming the Mask AP of vanilla bounding boxes. Guided
segmentation of extreme points further improves this to 32.1% Mask AP. We
applied the PuckNet vision system to the SuperTuxKart video game to test it's
capacity to achieve competitive play in dynamic and co-operative multiplayer
environments.
