Machine Learning with Uncertainty for Monitoring Moving Objects and People

用于监控移动物体和人员的不确定性机器学习

• 批准号: RGPIN-2020-04417
• 负责人: Bolic, Miodrag
• 金额: $ 2.04万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752474
• 关键词: Machine; Learning; Uncertainty; Monitoring; Moving

Sequence data is very common in the natural world, emerging in myriad datasets such as stock markets, weather, biomedical, target tracking and so on. Often the sequences are composed of multiple channels of correlated information that change together in time. State of the art deep learning models of sequence data represent data using scalars, rather than random variables. These scalar-based deep learning models require large amounts of data to train, perform poorly when trained on data from one domain and then applied to a different domain, and model rare events poorly. Traditional statistical models for sequence data are not learning-based and very often rely on fixed parameters that should be adjusted for different environments or different situations. Bayesian probabilistic models allow for designing interpretable systems, dealing with small data and quantifying uncertainties in estimations and predictions. New probabilistic deep learning frameworks have primed this field for advancement. Research on taking advantage of both Bayesian probabilistic and deep learning models has just started recently. In this program, we plan to significantly contribute towards developing probabilistic deep learning models for time series and sequence data. Initial work in this field resulted in powerful, versatile but very complex models. We plan to develop simpler models and quality metrics, and to make them easier to train as well as to implement the model in real-time in practical applications. We will test the developed model building approaches on at least two practical applications: monitoring the activities and intents of people with the focus on monitoring elderly people, as well as classifying and tracking multiple UAVs and inferring their intents. For data collection, we plan to use the same sensors for both applications, and a very similar algorithmic framework. Recognizing intents is extremely important because this brings machine learning closer to the way that humans observe the behaviour of other humans and moving objects. This research will move the field of machine learning by integrating multiple concepts including Bayesian probabilistic learning, time series, deep learning, and uncertainty quantification. The benefits to Canada and the world would be tremendous if the developed model building framework outperforms the state of the art, and additional benefits to Canada are the training of ten students in a highly sought after field, and new applications advancing the care of elderly people and improving public safety by more accurately monitoring the risk posed by objects in motion.

序列数据在自然世界中非常普遍，在诸如股票市场，天气，生物医学，目标跟踪等无数数据集中出现。通常，序列由多个相关信息的多个通道组成，这些信息会随着时间的流逝而变化。序列数据的艺术状态深度学习模型代表使用标量而不是随机变量的数据。这些基于标量的深度学习模型需要大量的数据进行训练，在对一个域的数据进行培训，然后应用于另一个领域，并对罕见事件进行训练。序列数据的传统统计模型不是基于学习的，并且通常依赖于应针对不同环境或不同情况进行调整的固定参数。贝叶斯概率模型允许设计可解释的系统，处理较小的数据并在估计和预测中量化不确定性。新的概率深度学习框架已为这一领域提供了准备。关于利用贝叶斯概率和深度学习模型的研究刚刚开始。 在该计划中，我们计划为开发时间序列和序列数据开发概率深度学习模型做出重大贡献。该领域的初始工作导致了功能强大，通用但非常复杂的模型。我们计划开发更简单的模型和质量指标，并使它们更易于训练，并在实用应用中实时实施该模型。 我们将在至少两个实际应用上测试开发的模型构建方法：监视人们的活动和意图，重点是监视老年人，并分类和跟踪多个无人机并推断他们的意图。对于数据收集，我们计划在两个应用程序和非常相似的算法框架上使用相同的传感器。认识意图非常重要，因为这种使机器学习更接近人类观察其他人的行为和移动物体的行为。 这项研究将通过整合包括贝叶斯概率学习，时间序列，深度学习和不确定性量化在内的多个概念来移动机器学习领域。如果开发的模型构建框架优于艺术状况，对加拿大和世界的好处将是巨大的，而加拿大的其他好处是在一个备受追捧的领域中对十名学生的培训，以及通过更准确地监控对象在运动中带来的风险来改善公共安全的新应用。