Resource-Efficient Salient Foreground Detection for Embedded Smart Cameras br Tracking Feedback

Mauricio Casares, Senem Velipasalar
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引用次数: 14

Abstract

Battery-powered wireless embedded smart cameras havelimited processing power, memory and energy. Since videoprocessing tasks consume significant amount of power,the problem of limited resources becomes even more pro-nounced, and necessitates designing light-weight algo-rithms suitable for embedded platforms. In this paper, wepresent a resource-efficient salient foreground detection andtracking algorithm. Contrary to traditional methods thatimplement foreground object detection and tracking inde-pendently and in a sequential manner, the proposed methoduses the feedback from the tracking stage in the foregroundobject detection. We compare the proposed method with asequential method on the microprocessor of an embeddedsmart camera, and present the savings in the processingtime and energy consumption and the gain in the lifetimeof a battery-powered camera for different scenarios. Thepresented method provides significant savings in terms ofthe processing time of a frame. We take advantage of thesesavings by sending the microprocessor to idle state at theend of processing a frame, and when the scene is empty.
基于跟踪反馈的嵌入式智能相机显著前景检测
电池供电的无线嵌入式智能相机的处理能力、内存和能量有限。由于视频处理任务消耗大量的功率,资源有限的问题变得更加明显,并且需要设计适合嵌入式平台的轻量级算法。本文提出了一种资源高效的显著前景检测与跟踪算法。传统的前景目标检测和跟踪方法是独立地、顺序地实现的,而本文提出的方法将跟踪阶段的反馈信息应用到前景目标检测中。我们将该方法与嵌入式智能相机微处理器上的顺序方法进行了比较,并给出了不同场景下电池供电相机处理时间和能耗的节省以及寿命的增加。所提出的方法在帧的处理时间方面提供了显著的节省。我们利用这些节省,在处理帧结束时,当场景为空时,将微处理器发送到空闲状态。
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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