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

生物科学最近取得了长足的进步，既利用了自动化的数据收集方法，又使研究人员能够通过公共数据库有效地分享结果。该项目将为使用视频跟踪动物、细胞或机器人的运动的应用程序实现同样的目标，方法是制作免费可用的软件，以高效和自动地提取运动数据，并将这些数据直接添加到公共数据库(KNB数据存储库)。该软件将易于使用并适应新类型的视频，这些问题到目前为止一直是广泛采用现有视频跟踪工具的障碍。共享运动数据和视频的能力将促进研究人员之间的合作。这两项功能将使动物群体行为、行为遗传学、细胞生物学和集体机器人等领域以及记录许多个人行动的其他领域取得根本上的新进展。该软件由于易于使用并能够从数据库中获取研究视频，也将作为K-12和大学一级的教学工具。此外，该项目将用于培训几名生物学和计算机科学方面的大学生和研究生；这种跨学科培训对于当今生物学研究的进步是必不可少的。该项目将实施明确的、当前的图形用户界面设计的独特组合，以提高可用性和最先进的机器学习技术，以提高运动跟踪的准确性。此外，开发的软件将使用户能够将结果可视化以进行验证和分析，并包括用户纠正任何剩余跟踪错误的功能。这将使用户能够获得科学质量的数据输出，而不必使用多个软件应用程序，也不必手动对数据文件进行后处理。在该项目的背景下，将举办几个讲习班，并开发一个网站，以改善生物学学生和研究人员的可及性。该项目还将开发与现有KNB科学数据储存库的直接链接，以便用户可以访问该储存库、比较其结果或轻松完成荟萃分析。除了推进生物学研究，这还将通过提供大量具有准确用户注释的视频集合，为计算机视觉科学家产生广泛的资源，以改进目标检测和跟踪等核心算法。欲了解更多信息，请访问http://www.abctracker.org.。

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.