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Time characterization of video acquisition flow

机译:视频采集流程的时间特征

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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 Intempora company.
机译:本文为视频数据采集流程的时间表征做出了贡献。视频流被用于车辆中的许多机器人技术和嵌入式系统。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,环境的数据捕获在于在给定时刻对该环境进行采样。当这些数据在其内部存储器中可用时,嵌入式计算机将能够临时标记这些数据。因此,从传感器采样时刻到计算机内存中图像存在的时间间隔是未知的。这种延迟会在同一场景上引起时间上的移位,该时间上的移位由多种类型的传感器(超声波,遥测仪等)看到。这些各种传感器的数据融合将变得更加困难,并在决策过程中引起重要的降级。因此,重要的是能够表征摄像机的等待时间并更精确地重新同步各个传感器的所有数据。在本文中,我们提出了一种解决方案,以表征视频采集系统流程的延迟。与Intempora公司合作完成了这项工作。

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