论文标题
在复杂的人体组合中，手动指导的高分辨率功能增强了用于细粒的原子动作分割
Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies
论文作者
论文摘要
由于复杂的人体组装原子能的快速时间和细粒度的快速性质，传统的动作分割方法需要对视频框架进行空间（通常是时间）的降低采样，通常会在制造业领域内使用准确的精确分类所需的至关重要的精细粒度空间和时间信息。为了充分利用更高分辨率的视频数据（通常在制造域内收集），并促进实时精确的动作细分 - 人类机器人协作所需的 - 我们提供了一种新颖的手工位置指导的高分辨率高分辨率功能增强模型。我们还提出了一种简单但有效的方法，即通过在推理时使用训练和暂时意识到的标签清洁，以实时行动进行离线训练的动作识别模型，以对时间短的细颗粒动作进行实时动作分割。我们在一个新的动作分割数据集上评估了我们的模型，该数据集包含24个（+背景）原子能从现实世界机器人组装生产线的视频数据中进行评估。显示高分辨率的手功能以及传统的框架宽功能可以改善细粒度的原子动作分类，尽管暂时意识到的标签清除了我们的模型，但我们的模型能够超过相似的编码器/解码器方法，同时允许实时分类。
Due to the rapid temporal and fine-grained nature of complex human assembly atomic actions, traditional action segmentation approaches requiring the spatial (and often temporal) down sampling of video frames often loose vital fine-grained spatial and temporal information required for accurate classification within the manufacturing domain. In order to fully utilise higher resolution video data (often collected within the manufacturing domain) and facilitate real time accurate action segmentation - required for human robot collaboration - we present a novel hand location guided high resolution feature enhanced model. We also propose a simple yet effective method of deploying offline trained action recognition models for real time action segmentation on temporally short fine-grained actions, through the use of surround sampling while training and temporally aware label cleaning at inference. We evaluate our model on a novel action segmentation dataset containing 24 (+background) atomic actions from video data of a real world robotics assembly production line. Showing both high resolution hand features as well as traditional frame wide features improve fine-grained atomic action classification, and that though temporally aware label clearing our model is capable of surpassing similar encoder/decoder methods, while allowing for real time classification.
