The past decade has seen an increased interest in human activity recognition. Most commonly, the raw data coming from sensors attached to body parts are unannotated, which creates a need for fast labelling method. Part of the procedure is choosing or designing an appropriate performance measure. We propose a new performance measure, the Locally Time-Shifted Measure, which addresses the issue of timing uncertainty of state transitions in the classification result. Our main contribution is a novel post-processing method for binary activity recognition. It improves the accuracy of the classification methods, by correcting for unrealistically short activities in the estimate.
翻译:在过去的十年中,人们对人类活动的认识越来越感兴趣,最常见的是,人体部分附属传感器提供的原始数据没有附加说明,这就要求采用快速标签方法,部分程序是选择或设计适当的性能衡量标准,我们提出了新的绩效衡量标准,即 " 局部时间差措施 ",解决国家分类结果过渡时间不确定的问题,我们的主要贡献是采用新的二元活动确认后处理方法,通过纠正估计数中不切实际的短期活动,提高分类方法的准确性。