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许多领域都很重要，从用户建模和人机交互到图形和动画。在涉及人类身体限制、慢性残疾者（如中风后、ALS、类风湿关节炎、脑瘫等）的研究中，良好的注释数据集和注释工具资源尤为重要。目前，还没有一个包含人类活动数据和分析工具的综合性社区生态评价。新方法和算法的发展将改进和实现现实世界的运动跟踪应用程序，但却受到一个固有困难的阻碍：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、人机交互、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等方面的专家，以确定在构建自动准确注释的人类运动视频数据存储库时应该收集和处理的特定人类活动数据。具体来说，一个自动准确注释的人体运动视频数据库有可能影响几个学科的研究，使用人体运动跟踪和识别，通过显着提高识别评估环境，药物，人类心理融合的行为标记的能力，从而扩展临床和心理领域。然而，由于大数据问题（人类行为的多感官视觉输入所产生的数量、速度、种类、准确性），自动化数据收集，特别是注释的任务是非简单的。传统上，人体运动视频数据的采集和注释都是手工进行的。由于视频传感器产生的数据量很大，这项工作非常繁琐且容易出错。由于这个原因，大多数现有的数据集都非常小，并且专门用于收集它们的问题。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CRI人类活动存储库的输入。将组织一次由应邀专家参加的讲习班，这些专家将帮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定义拟议的Smart-Move存储库的特征和设施，这是一个时空注释的人体运动存储库，重点关注以下方面：(1)确定要捕获的具体活动和多感官数据收集方法；(2)对采集数据进行准确实时标注和存储的工具；(3)搜索设施，使研究人员能够贡献和提供反馈；(4)事件识别和数据汇总工具，使研究人员能够自动分析自己的新大数据；(5)采用策略性选择的多流数据集进行数据收集和分析，以保持整体项目成本较低的可行性；(6)不同传感器或不同采样频率采集的异质人类活动数据建模的局限性。

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.