Collaborative Research: ABI Development: A User-friendly Tool for Highly Accurate Video Tracking

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Biological science has made great strides recently both by capitalizing on automated methods of data collection and by enabling researchers to share results efficiently via public databases. This project will achieve the same for applications that use videos to track movement of animals, cells, or robots, by producing freely available software for efficient and automatic extraction of movement data and directly adding such data to a public database (the KNB Data Repository). The software will be easy to use and adapt to new types of videos, issues that have so far been roadblocks to widespread adoption of existing video tracking tools. The ability to share both movement data and videos will stimulate collaboration among researchers. Both functionalities will enable fundamentally new advances in such areas as animal group behavior, behavioral genetics, cell biology, and collective robotics, and other fields that record the movements of many individuals. The software, because of its ease of use and enabled access to research videos from the database, will also serve as a tool in teaching at the K-12 and college level. In addition, this project will serve to train several college and graduate students in both biology and computer science; such interdisciplinary training is essential for advances in biological research today.This project will implement a unique combination of clear, current graphical user interface design to improve usability with state-of-the-art machine learning techniques to improve movement tracking accuracy. In addition, the developed software will enable users to visualize results for validation and analysis, and include functionality for users to correct any remaining tracking errors. This will enable users to get scientific-quality data output without having to employ multiple software applications and without having to manually post-process data files. In the context of the project, several workshops will be held and a website developed to improve accessibility for students and researchers in biology. The project will also develop a direct link to the existing KNB scientific data repository, such that users can access the repository, compare their results, or complete meta-analyses easily. Besides advancing biological research, this will also generate an extensive resource for computer vision scientists by providing a large collection of videos with accurate user annotation for improving core algorithms such as object detection and tracking. More information may be found at http://www.abctracker.org.