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Sensor fusion to enable next generation low cost Night Vision systems

机译:传感器融合使下一代低成本夜视系统

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The next generation of automotive Night Vision Enhancement systems offers automatic pedestrian recognition with a performance beyond current Night Vision systems at a lower cost. This will allow high market penetration, covering the luxury as well as compact car segments.Improved performance can be achieved by fusing a Far Infrared (FIR) sensor with a Near Infrared (NIR) sensor.However, fusing with today's FIR systems will be too costly to get a high market penetration. The main cost drivers of the FIR system are its resolution and its sensitivity. Sensor cost is largely determined by sensor die size. Fewer and smaller pixels will reduce die size but also resolution and sensitivity. Sensitivity limits are mainly determined by inclement weather performance. Sensitivity requirements should be matched to the possibilities of low cost FIR optics,especially implications of molding of highly complex optical surfaces. As a FIR sensor specified for fusion can have lower resolution as well as lower sensitivity, fusing FIR and NIR can solve performance and cost problems.To allow compensation of FIR-sensor degradation on the pedestrian detection capabilities, a fusion approach called MultiSensorBoosting is presented that produces a classifier holding highly discriminative sub-pixel features from both sensors at once. The algorithm is applied on data with different resolution and on data obtained from cameras with varying optics to incorporate various sensor sensitivities. As it is not feasible to record representative data with all different sensor configurations, transformation routines on existing high resolution data recorded with high sensitivity cameras are investigated in order to determine the effects of lower resolution and lower sensitivity to the overall detection performance. This paper also gives an overview of the first results showing that a reduction of FIR sensor resolution can be compensated using fusion techniques and a reduction of sensitivity can be compensated.
机译:下一代汽车夜视增强系统提供自动行人识别,其性能超越当前夜视系统,成本较低。这将允许高位市场渗透,覆盖奢侈品以及紧凑的汽车段。通过融合远红外(FIR)传感器可以使用近红外(NIR)传感器来实现的性能。然而,与当今的FIR系统一起融合昂贵的市场渗透率昂贵。 FIR系统的主要成本驱动因素是其分辨率及其敏感性。传感器成本主要由传感器芯片尺寸决定。较少且较小的像素会降低芯片尺寸,但也可以分辨率和灵敏度。敏感性限制主要由恶劣天气性能决定。敏感性要求应与低成本灭火光学器件的可能性相匹配,特别是高度复杂的光学表面的模塑的含义。由于为融合指定的FIR传感器可能具有较低的分辨率以及较低的灵敏度,融合FIR和NIR可以解决性能和成本问题。允许对行人检测能力进行补偿,提出了一种称为多思索船的融合方法产生一个分类器,一次保持来自两个传感器的高鉴别性子像素特征。该算法应用于具有不同分辨率的数据和从具有不同光学器件获得的数据,以结合各种传感器灵敏度。由于记录具有所有不同传感器配置的代表性数据是不可行的,因此研究了用高灵敏度摄像机记录的现有高分辨率数据的转换例程,以确定较低分辨率和对整体检测性能的敏感性较低的效果。本文还概述了第一结果,表明可以使用融合技术补偿冷杉传感器分辨率的减少,并且可以补偿灵敏度的降低。

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