Learning to Track at 100 FPS with Deep Regression Networks Learning to Track at 100 FPS with Deep Regression Networks
Paper summary This paper introduces a bunch of tricks which make sense for visual tracking. These tricks are as followed: 1. At test time, a crop with center at the previous frame's bounding box's center with size larger than the bounding box is given along with the search area in the current frame. 2. Training offline on a large set of videos (where object bounding boxes are given for a subset of frames) and images with object bounding boxes. 3. Network takes two images: i) a crop of the image/frame around the bounding box and ii) the image centered at the center of the bounding box. Given the later, network regresses the bounding box in i). 4. Above crops are sampled such that the ground truth bounding box center in i) is not very far from the center in ii), hence network prefers smooth motion. My take: This is very nice way to use still images to train image correlation task and hence can be used for tracking. Speed on gpu is very impressive but still not comparable on CPUs.
arxiv.org
arxiv-sanity.com
scholar.google.com
Learning to Track at 100 FPS with Deep Regression Networks
David Held and Sebastian Thrun and Silvio Savarese
arXiv e-Print archive - 2016 via arXiv
Keywords: cs.CV, cs.AI, cs.LG, cs.RO

more

Loading...
Your comment:


Short Science allows researchers to publish paper summaries that are voted on and ranked!
About