This blog post is a follow-up to the Hacky Hour presented on 12/3/2020, Build a Virtual Green Screen with Semantic Segmentation.

Recent trends in computation and automation highlight a massive and sustained growth for computer vision based applications on the edge. Market research also points to tremendous interest in these applications amongst entrepreneurs and developers alike. However, developing commercial-grade Computer Vision applications for the edge is hard (link to the new article here). There are two primary reasons for this.

As you most likely noticed in the image above, the edges generated by this model are fairly large. In a subsequent tutorial, I’ll cover how to smooth these edges for a less blocky look!

Detecting pedestrians and bicyclists in a cityscape scene is a crucial part of autonomous driving applications. Autonomous vehicles need to determine how far away pedestrians and bicyclists are, as well as what their intentions are. A simple way to detect people and bicycles is to use Object Detection. However, in this case we need much more detailed information about the exact locations of the pedestrians and bicyclists than Object Detection can provide, so we’ll use a technique called ...