Follow Anything: Open-set detection, tracking, and following in
  real-time
Authors: name: Alaa Maalouf, name: Ninad Jadhav, name: Krishna Murthy Jatavallabhula, name: Makram Chahine, name: Daniel M. Vogt, name: Robert J. Wood, name: Antonio Torralba, name: Daniela Rus

Tracking and following objects of interest is critical to several robotics
use cases, ranging from industrial automation to logistics and warehousing, to
healthcare and security. In this paper, we present a robotic system to detect,
track, and follow any object in real-time. Our approach, dubbed ``follow
anything'' (FAn), is an open-vocabulary and multimodal model -- it is not
restricted to concepts seen at training time and can be applied to novel
classes at inference time using text, images, or click queries. Leveraging rich
visual descriptors from large-scale pre-trained models (foundation models), FAn
can detect and segment objects by matching multimodal queries (text, images,
clicks) against an input image sequence. These detected and segmented objects
are tracked across image frames, all while accounting for occlusion and object
re-emergence. We demonstrate FAn on a real-world robotic system (a micro aerial
vehicle) and report its ability to seamlessly follow the objects of interest in
a real-time control loop. FAn can be deployed on a laptop with a lightweight
(6-8 GB) graphics card, achieving a throughput of 6-20 frames per second. To
enable rapid adoption, deployment, and extensibility, we open-source all our
code on our project webpage at https://github.com/alaamaalouf/FollowAnything .
We also encourage the reader the watch our 5-minutes explainer video in this
https://www.youtube.com/watch?v=6Mgt3EPytrw .