BlinkBud: 通过单个耳塞上的采样单目3D检测检测后方危险
BlinkBud: Detecting Hazards from Behind via Sampled Monocular 3D Detection on a Single Earbud
AI总结：
BlinkBud通过单耳塞和手机实现后方危险检测，采用3D跟踪算法和强化学习优化功耗与精度。
摘要：
未能意识到从后方接近的高速车辆对行人和骑行者的道路安全构成巨大威胁。在本文中，我们提出了BlinkBud，它利用一个耳塞和一部配对的手机，实时检测用户后方接近的危险物体。核心思想是利用耳塞拍摄的少量采样摄像头图像来准确跟踪视觉识别的物体。为了在保证最佳跟踪精度的同时最小化耳塞和手机的功耗，我们设计了一种新的3D物体跟踪算法，结合基于卡尔曼滤波的轨迹估计方案和基于强化学习的最优图像采样策略。此外，通过利用估计的仰角和偏航角来校正物体深度估计，并将相机坐标系统对齐到用户身体坐标系统，从而显著消除了用户头部持续移动对跟踪精度的影响。我们实现了一个BlinkBud原型系统并进行了广泛的现实世界实验。结果表明，BlinkBud非常轻量，具有超低的平均功耗，分别为耳塞和智能手机29.8 mW和702.6 mW，并且可以以低的平均假阳性率（FPR）和假阴性率（FNR）（分别为4.90%和1.47%）准确检测危险。
英文摘要：
Failing to be aware of speeding vehicles approaching from behind poses a huge threat to the road safety of pedestrians and cyclists. In this paper, we propose BlinkBud, which utilizes a single earbud and a paired phone to online detect hazardous objects approaching from behind of a user. The core idea is to accurately track visually identified objects utilizing a small number of sampled camera images taken from the earbud. To minimize the power consumption of the earbud and the phone while guaranteeing the best tracking accuracy, a novel 3D object tracking algorithm is devised, integrating both a Kalman filter based trajectory estimation scheme and an optimal image sampling strategy based on reinforcement learning. Moreover, the impact of constant user head movements on the tracking accuracy is significantly eliminated by leveraging the estimated pitch and yaw angles to correct the object depth estimation and align the camera coordinate system to the user's body coordinate system, respectively. We implement a prototype BlinkBud system and conduct extensive real-world experiments. Results show that BlinkBud is lightweight with ultra-low mean power consumptions of 29.8 mW and 702.6 mW on the earbud and smartphone, respectively, and can accurately detect hazards with a low average false positive ratio (FPR) and false negative ratio (FNR) of 4.90% and 1.47%, respectively.
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。
