CI-P: Planning for SMART-MOVE: A Spatiotemporal Annotated Human Activity Repository for Advanced Motion Recognition and Analysis Research

准确的人体运动跟踪和活动识别对于支持计算机科学和工程研究的众多领域非常重要，从用户建模和人类机器人交互到图形和动画。良好的注释数据集和注释工具资源在涉及人体生理限制、慢性残疾人(如中风后、肌萎缩侧索硬化症、类风湿性关节炎、脑瘫等)的研究中尤为重要。目前，还没有一个全面的社区CRI，包括人类活动数据和分析工具。新方法和算法的开发将改进和实现真实世界的运动跟踪应用程序，但存在一个固有的困难：缺乏大量的训练和测试数据。该规划活动汇集了计算机视觉、机器学习、数据挖掘、用户界面设计、辅助环境、人类机器人交互、数据库研究、涉及实时人类活动的大数据、治疗师、临床医生、设备制造商和传感器开发人员的专家，以便识别在建立自动且准确地注释的人体运动视频数据的储存库时应该收集和处理的特定人类活动数据。具体地说，人体运动的自动且准确地注释的视频数据的储存库通过显著提高识别用于评估环境、药物、人类心理学，从而扩展了临床和心理学领域。然而，由于大数据问题(由于人类行为的多感官视觉输入而产生的数量、速度、多样性、准确性)，自动化数据收集尤其是注释的任务并不是微不足道的。传统上，人体运动的视频数据是人工采集和标注的。由于视频传感器产生的数据量很大，这项任务非常繁琐，而且容易出错。出于这个原因，大多数现有的数据集都非常小，并且专门针对收集它们所针对的问题。该项目将从研究用户社区收集关于如何最好地开发这样一个人类活动CRI储存库的意见。将组织一次研讨会，邀请专家协助确定应收集和处理的具体人类活动数据。研讨会期间征求的意见将有助于确定拟议的Smart-Move储存库的功能和设施，这是一个时空注释的人体运动储存库，重点放在以下方面：(1)确定要捕获的具体活动和多感官数据收集方法；(2)准确实时注释和存储收集的数据的工具；(3)使研究人员能够贡献和提供反馈的搜索设施；(4)事件识别和数据汇总工具，使研究人员能够自动分析他们自己的新大数据；(5)建议的数据收集和分析的可行性，使用策略性地选择的一组多流数据以保持总体项目成本较低；(6)对由不同传感器或不同采样频率收集的不同类型的人类活动数据进行建模时的限制。

Accurate human motion tracking and activity recognition are important in supporting numerous areas of computer science and engineering research, ranging from user modeling and human robot interaction to graphics and animation. A good resource of annotated datasets and annotation tools is particularly important in research involving physical human limitations, persons with chronic disabilities, such as post-stroke, ALS, Rheumatoid Arthritis, Cerebral Palsy, and others. Currently, a comprehensive community CRI with human activity data and analysis tools is not in place. The development of new methods and algorithms that will improve and enable real-world motion tracking applications is hampered by an inherent difficulty: the lack of large sets of training and testing data. This planning activity brings together experts in computer vision, machine learning, data mining, user interface design, assistive environments, human robot interaction, databases research, big data involving real time human activity, therapists, clinicians, device makers and sensor developers in order to identify the specific human activity data that should be collected and processed when building a repository of automatically and accurately annotated video data of human motion.Specifically, a repository of automatically and accurately annotated video data of human motion has the potential to impact the research of several disciplines using human motion tracking and recognition by significantly improving the ability to recognize behavioral markers for assessing the confluence of environment, drugs, human psychology and thus extend clinical and psychological areas. However, due to big data issues (volume, velocity, variety, veracity, arising from multisensory visual input of human behavior), the tasks of automating data collection and especially annotation are non-trivial. Traditionally, video data of human motion are collected and annotated manually. Due to the large amount of data generated by video sensors, this task is very tedious and error prone. For this reason, most existing datasets are very small and specialized to the problem for which they were collected. This project will gather input from the research user communities on how to best develop such a CRI repository of human activity. A workshop will be organized with invited experts who will help identify the specific human activity data that should be collected and processed. The input solicited during the workshop will help define the features and facilities of the proposed Smart-Move repository, a spatiotemporal annotated human motion repository by focusing on the following: (1) determining the specific activities to be captured and the methods of multisensory data collection; (2) tools for accurate real-time annotation and storage of the collected data; (3) search facilities to enable researchers to contribute and provide feedback; (4) event recognition and data summarization tools to enable researchers to analyze their own new big data automatically; (5) feasibility of the proposed data collection and analysis using a strategically chosen set of multi-stream data to keep the overall project cost low; (6) limitations when modeling heterogeneous types of human activity data collected by different sensors or of different sample frequencies.