BluePark: tracking parking and un-parking events in indoor garages

Sonia Soubam, Dipyaman Banerjee, Vinayak Naik, D. Chakraborty
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引用次数: 10

Abstract

Finding a parking spot in a busy indoor parking lot is a daunting task. Retracing a parked vehicle can be equally frustrating. We present BluePark, a collaborative sensing mechanism using smartphone sensors to solve these problems in real-time, without any input from user. We propose a novel technique of combining accelerometer and WiFi data to detect and localize parking and un-parking events in indoor parking lot. We validate our approach at the basement parking of a popular shopping mall. The proposed method outperforms Google Activity Recognition API by 20% in detecting drive state in indoor parking lot. Our experiments show 100% precision and recall for parking and un-parking detection events at low accelerometer sampling rate of 15Hz, irrespective of phone?s position. It has a low detection latency of 20s with probability of 0.9 and good location accuracy of 10m.
BluePark:跟踪室内车库的停车和不停车事件
在繁忙的室内停车场找到停车位是一项艰巨的任务。追踪一辆停着的车也同样令人沮丧。我们提出了BluePark,一种使用智能手机传感器的协作传感机制,可以实时解决这些问题,而无需用户的任何输入。本文提出了一种结合加速度计和WiFi数据的室内停车场停车和非停车事件检测和定位技术。我们在一家受欢迎的购物中心的地下停车场验证了我们的方法。该方法在检测室内停车场的驾驶状态方面比谷歌Activity Recognition API提高了20%。我们的实验显示,在15Hz的低加速度计采样率下,停车和非停车检测事件的准确率和召回率为100%,与手机无关。年代的位置。探测延迟低,20s,概率为0.9,定位精度达10m。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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