工业领域眼动视角手-物体交互检测的实时系统
A Real-Time System for Egocentric Hand-Object Interaction Detection in Industrial Domains
AI总结：
本文提出了一种实时系统，通过动作识别和物体检测模块检测工业领域中的手-物体交互，实现了高效准确的实时检测。
摘要：
手-物体交互检测在实时应用中仍然是一个开放性挑战，其中直观的用户体验依赖于快速且准确地检测与周围物体的交互。我们提出了一种高效的检测方法，用于从流式眼动视觉中实时检测手-物体交互。我们的方法包括一个动作识别模块和一个物体检测模块，用于在确认交互后识别活跃的物体。我们的动作识别模块采用Mamba模型，以EfficientNetV2为骨干网络，在ENIGMA-51基准测试中以30fps运行时达到38.52%的p-AP，而我们微调的YOLOWorld模型在手和物体检测上达到85.13%的AP。我们实现了一个级联架构，其中动作识别和物体检测模块按顺序操作。当动作识别预测出接触状态时，它会激活物体检测模块，该模块会针对相关帧进行推理，以检测和分类活跃的物体。
英文摘要：
Hand-object interaction detection remains an open challenge in real-time applications, where intuitive user experiences depend on fast and accurate detection of interactions with surrounding objects. We propose an efficient approach for detecting hand-objects interactions from streaming egocentric vision that operates in real time. Our approach consists of an action recognition module and an object detection module for identifying active objects upon confirmed interaction. Our Mamba model with EfficientNetV2 as backbone for action recognition achieves 38.52% p-AP on the ENIGMA-51 benchmark at 30fps, while our fine-tuned YOLOWorld reaches 85.13% AP for hand and object. We implement our models in a cascaded architecture where the action recognition and object detection modules operate sequentially. When the action recognition predicts a contact state, it activates the object detection module, which in turn performs inference on the relevant frame to detect and classify the active object.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
