论文标题
人类活动细分和人体工程学风险评估的多任务学习方法
A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment
论文作者
论文摘要
我们使用基于图的多任务建模在长视频中提出了一种新的人力活动评估方法（HAE）。活动评估的先前工作要么使用检测到的骨骼直接计算度量，要么使用场景信息来回归活动评分。这些方法不足以进行准确的活动评估，因为它们仅在剪辑上计算平均得分，并且不考虑关节和身体动力学之间的相关性。此外，它们是高度依赖场景的，这使这些方法的普遍性值得怀疑。我们为HAE提出了一个新型的多任务框架，该框架利用图形卷积网络骨架将人类关节之间的互连嵌入特征中。在此框架中，我们解决了人类活动分割（HAS）问题，作为改善活动评估的辅助任务。 Hes Head由编码器派定时间卷积网络提供动力，将语义上的长视频分为不同的活动类别，而HAE则使用基于长期记忆的架构。我们在UW-IOM和TUM厨房数据集上评估了我们的方法，并讨论了这两个数据集中的成功和故障案例。
We propose a new approach to Human Activity Evaluation (HAE) in long videos using graph-based multi-task modeling. Previous works in activity evaluation either directly compute a metric using a detected skeleton or use the scene information to regress the activity score. These approaches are insufficient for accurate activity assessment since they only compute an average score over a clip, and do not consider the correlation between the joints and body dynamics. Moreover, they are highly scene-dependent which makes the generalizability of these methods questionable. We propose a novel multi-task framework for HAE that utilizes a Graph Convolutional Network backbone to embed the interconnections between human joints in the features. In this framework, we solve the Human Activity Segmentation (HAS) problem as an auxiliary task to improve activity assessment. The HAS head is powered by an Encoder-Decoder Temporal Convolutional Network to semantically segment long videos into distinct activity classes, whereas, HAE uses a Long-Short-Term-Memory-based architecture. We evaluate our method on the UW-IOM and TUM Kitchen datasets and discuss the success and failure cases in these two datasets.
