Achieving Competitive Play Through Bottom-Up Approach in Semantic Segmentation
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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 points estimated directly resolve into a rectangular object mask, with a COCO Mask AP of 17.6 segmentation of extreme points further improves this to 32.1 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.
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