Abstract
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.
Abstract (translated)
未能察觉后方快速接近的车辆对行人和骑自行车者构成了巨大的道路安全威胁。在本文中，我们提出了BlinkBud系统，该系统利用单个耳塞和配对手机来实时检测用户后方接近的危险物体。核心思想是通过少量从耳塞拍摄的照片样本准确追踪视觉识别的对象。 为了在保证最佳跟踪精度的同时最小化耳塞和手机的能耗，设计了一种新型的3D对象跟踪算法，该算法结合了基于卡尔曼滤波器的轨迹估计方案以及一种基于强化学习的最佳图像采样策略。此外，通过利用预估的角度（俯仰角和偏航角）来校正物体深度估算并将相机坐标系对准用户的体感坐标系，显著消除了持续头部运动对跟踪精度的影响。 我们实现了一个原型BlinkBud系统，并进行了广泛的实地实验。结果表明，BlinkBud轻量级且能耗极低，在耳塞和智能手机上的平均功率消耗分别为29.8毫瓦和702.6毫瓦；该系统能够以较低的平均假阳性率（FPR）和假阴性率（FNR），即4.90% 和 1.47%，准确地检测出危险。
URL
https://arxiv.org/abs/2512.01366
