An Efficient Data Imputation Technique for Human Activity Recognition
Instituto de Telecomunicações（电信研究所）；Universidade da Beira Interior（贝拉内华大学）；Polytechnic Institute of Viseu（维塞乌理工学院）；Department of Computer Engineering（计算机工程系）；University of Engineering and Technology (UET)（工程与技术大学）；Faculty of Computer Science and Engineering（计算机科学与工程学院）；University Ss Cyril and Methodius（圣西里尔与美多德大学）
Ivan Miguel Pires, Faisal Hussain, Nuno M. Garcia, Eftim Zdravevski
备注：
8 Pages, 8 Figures, 1 Table. Accepted in 14th Multi Conference on Computer Science and Information Systems 2020 (MCCSIS 2020)
The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset.
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset
瞬间捕捉了我们生活的很大一部分。准确识别这些时刻是具有挑战性的，因为这些时刻的解释是多样和复杂的。动作识别是指对给定视频中存在的期望动作/活动进行分类的动作。在这项工作中，我们在Moments in Time数据集上进行实验，以准确识别3秒片段中发生的活动。我们使用最先进的技术进行视觉，听觉和时空定位，并开发方法来准确地对时间数据集的时刻中的活动进行分类。我们使用基于视觉的文本特征和融合技术的新方法表现良好，在20个类别上提供了89.23%的前5名准确率-这是对基线TRN模型的显着改进。
Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition
AASS, Örebro University（厄勒布鲁大学AASS研究中心）；Faculty of Information Science and Electrical Engineering, Kyushu University（九州大学情报科学与电气工程学院）；ETSIDI, Universidad Politéctina de Madrid（马德里理工大学高等航空工程技术学院）
本文提出含新型基于活动的用户判别任务的对抗深度学习框架，以缓解 HAR 中的人际差异，并在三个数据集的 LOOCV 基准上取得优于以往方法和任务的分类效果。
