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Time Characterization of video acquisition flow

机译:视频采集流的时间特征

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This paper presents a contribution to time characterization for video data acquisition flow. Video flow is used in many robotics and embedded systems in vehicles. For real time applications, the delay of a video acquisition is very slow compared with other sensors. Indeed, the data capture of an environment consists in sampling this environment at a given moment. The embedded computer will be able to mark temporally these pieces of data when they are available in its internal memory. The time lag, between the moment of sampling in the sensor and the presence of the image in the computer memory is thus unknown. This delay induces a temporal shift on the same scene seen by several sensors of various types (ultrasonic, telemeter, etc). The fusion of data of these various sensors will be then more difficult and induces important degradation over the decision. It is thus important to be able to characterize this latency time of the cameras and to resynchronize more precisely all the data of the various sensors. In this paper, we propose a solution to characterize the latency of the video acquisition system flow. This work was completed in collaboration with Intem-pora company.
机译:本文提出了对视频数据采集流程的时间表征的贡献。视频流在许多机器人和车辆中的嵌入式系统中使用。对于实时应用,与其他传感器相比,视频采集的延迟非常慢。实际上,环境的数据捕获包括在给定时刻对该环境进行采样。当嵌入式计算机在其内部存储器中可用时,嵌入式计算机将能够暂时标记这些数据。因此,在传感器中的采样时刻和计算机存储器中的图像的存在之间的时间延迟因此未知。该延迟在各种类型(超声波,遥测等)的几个传感器上看到的同一场景中的时间转移。然后,这些各种传感器的数据的融合将更加困难,并在决定上引起重要的降低。因此,能够表征相机的这种潜伏时间并更精确地重新同步各种传感器的所有数据。在本文中,我们提出了一种解决方案来表征视频采集系统流的延迟。这项工作与Intem-Pora公司合作完成。

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