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Automatic image capturing and processing for PetrolWatch

机译:Petrolwatch自动图像捕获和处理

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In our previous work [1], we proposed a Participatory Sensing (PS) architecture called PetrolWatch to collect and share fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. A key part of the PetrolWatch architecture, and the main focus of this paper, is the automatic billboard image capture from a moving car without user intervention. We develop the system design and implementation of the automatic image collection for PetrolWatch. Capturing a clear image by an unassisted mobile phone from a moving car is proved to be a challenge by our street driving experiments. We design the camera control and image pre-selection schemes to address this challenge. In particular, we leverage the advanced capabilities of modern mobile phones to design an acceptable camera triggering range and set the camera focus accordingly. Experiment results show that our design improve fuel price extraction rate by more than 40%. To deal with blurred images caused by vehicle vibrations, we design a set of pre-selection thresholds based on the measures from embedded accelerometer of the mobile phone. Our experiments show that our pre-selection improves the system efficiency by eliminating 78.57% of the blurred images.
机译:在我们以前的工作[1]中,我们提出了一个名为Petrolwatch的参与式传感(PS)架构,从服务(或天然气)站的道路侧价格板(广告牌)的相机图像中收集和分享燃料价格。 Petrolwatch架构的一个关键部分,以及本文的主要焦点,是自动广告牌图像从没有用户干预的移动汽车捕获。我们开发了Petrolwatch自动图像集合的系统设计和实现。通过街道驾驶实验,证明了从移动车辆捕获一个独立的移动电话的清晰图像被证明是一项挑战。我们设计摄像机控制和图像预选方案以解决这一挑战。特别是,我们利用现代手机的高级功能来设计可接受的相机触发范围,并相应地设置相机对焦。实验结果表明,我们的设计将燃油价格提取率提高了40%以上。要处理由车辆振动引起的模糊图像,我们根据移动电话的嵌入式加速度计的措施设计一组预选阈值。我们的实验表明,我们的预选择通过消除78.57%的模糊图像来提高系统效率。

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