电梯、自动扶梯，还是两者皆非？利用智能手机在任意行人行为下分类输送设备状态
Elevator, Escalator, or Neither? Classifying Conveyor State Using Smartphone under Arbitrary Pedestrian Behavior
AI总结：
本文提出ELESON，一种利用智能手机惯性导航系统和对抗学习在任意行人行为下分类电梯、自动扶梯或两者皆非的轻量级深度学习方法，并在大规模数据集上取得高准确率和低资源消耗。
摘要：
了解行人的输送设备状态是“电梯”“自动扶梯”还是“两者皆非”，对于室内导航和人流管理等许多应用至关重要。以往关于输送设备状态分类的研究通常依赖专门设计的穿戴式传感器，或对行人行为做出强假设，这严重限制了其可部署性。为克服这一问题，我们利用普通智能手机的惯性导航系统（INS，包括加速度计、陀螺仪和磁力计）研究任意行人行为下的分类问题。该问题具有挑战性，因为输送设备状态的INS信号与任意且多样的行人行为相互纠缠。我们提出ELESON，一种新颖且轻量级的深度学习方法，利用手机INS将行人分类为电梯、自动扶梯或两者皆非。通过因果分解和对抗学习，ELESON提取与行人行为无关的输送设备状态的运动特征和磁场特征，并在此基础上借助证据分类器估计状态置信度。我们构建了一个大规模且多样化的数据集，包含36,420个行人在任意未知行为下随机乘坐电梯和自动扶梯的实例。大量实验表明，ELESON对行人行为具有鲁棒性，F1分数达到0.9以上的高准确率，AUROC（受试者工作特征曲线下面积）达到0.81的强置信度区分能力，并且计算和内存需求低，适合在普通智能手机上部署。
英文摘要：
Knowing a pedestrian's conveyor state of ''elevator,'' ''escalator,'' or ''neither'' is fundamental to many applications such as indoor navigation and people flow management. Previous studies on classifying the conveyor state often rely on specially designed body-worn sensors or make strong assumptions on pedestrian behaviors, which greatly strangles their deployability. To overcome this, we study the classification problem under arbitrary pedestrian behaviors using the inertial navigation system (INS) of the commonly available smartphones (including accelerometer, gyroscope, and magnetometer). This problem is challenging, because the INS signals of the conveyor states are entangled by the arbitrary and diverse pedestrian behaviors. We propose ELESON, a novel and lightweight deep-learning approach that uses phone INS to classify a pedestrian to elevator, escalator, or neither. Using causal decomposition and adversarial learning, ELESON extracts the motion and magnetic features of conveyor state independent of pedestrian behavior, based on which it estimates the state confidence by means of an evidential classifier. We curate a large and diverse dataset with 36,420 instances of pedestrians randomly taking elevators and escalators under arbitrary unknown behaviors. Our extensive experiments show that ELESON is robust against pedestrian behavior, achieving a high accuracy of over 0.9 in F1 score, strong confidence discriminability of 0.81 in AUROC (Area Under the Receiver Operating Characteristics), and low computational and memory requirements fit for common smartphone deployment.
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。
