海报摘要:用于嵌入式摄像机网络跟踪的高效背景减法

Yiran Shenn, W. Hu, Mingrui Yang, Junbin Liu, C. Chou
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引用次数: 2

摘要

背景减法通常是许多计算机视觉应用的第一步,例如物体定位和跟踪。它的目标是分割出场景中代表感兴趣对象的移动部分。在计算机视觉领域,研究人员一直致力于提高这种分割的鲁棒性和准确性,但他们的大多数方法都是计算密集型的,使得它们不适合我们的目标嵌入式相机平台,因为它的能量和处理能力明显受到更大的限制。为了解决这个问题并保持一个可接受的性能水平,我们将压缩感知(CS)引入到广泛使用的混合高斯中,以创建一种新的背景减法方法。结果表明,我们的方法不仅可以显着减少计算量(在DSP设置中减少7倍),而且仍然相当准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Poster abstract: Efficient background subtraction for tracking in embedded camera networks
Background subtraction is often the first step in many computer vision applications such as object localisation and tracking. It aims to segment out moving parts of a scene that represent object of interests. In the field of computer vision, researchers have dedicated their efforts to improve the robustness and accuracy of such segmentations but most of their methods are computationally intensive, making them nonviable options for our targeted embedded camera platform whose energy and processing power is significantly more con-strained. To address this problem as well as maintain an acceptable level of performance, we introduce Compressive Sensing (CS) to the widely used Mixture of Gaussian to create a new background subtraction method. The results show that our method not only can decrease the computation significantly (a factor of 7 in a DSP setting) but remains comparably accurate.
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