Visual multi-object tracking has the potential to accelerate many forms of quantitative analyses, especially in research communities investigating the motion, behavior, or social interactions within groups of animals. Despite its potential for increasing analysis throughput, complications related to accessibility, adaptability, accuracy, or scalable application arise with existing tracking systems. Several iterations of prototyping and testing have led us to a multi-object tracking system -- ABCTracker -- that is: accessible in both system as well as technical knowledge requirements, easily adaptable to new videos, and capable of producing accurate tracking data through a mixture of automatic and semi-automatic tracking features.
@article{arxiv.2001.10072,
title = {ABCTracker: an easy-to-use, cloud-based application for tracking multiple objects},
author = {Lance Rice and Samual Tate and David Farynyk and Joshua Sun and Greg Chism and Daniel Charbonneau and Thomas Fasciano and Anna Dornhaus and Min C. Shin},
journal= {arXiv preprint arXiv:2001.10072},
year = {2020}
}