Real-time computer vision tasks are emerging in consumer electronics with lightweight computing performance, which are an exquisite design art to balance the computational efficiency and accuracy. In this paper, we present the embedded background subtraction (EBGS) – an optimization algorithm for the entire process to increase computational efficiency and detection accuracy simultaneously. EBGS exploits a simple and efficient Additive Increase Multiplicative Decrease (AIMD) filter to improve the foreground detection accuracy without spending too much time. Moreover, the design combination between the contracted codebook background subtraction (BGS) model and a random model update is proposed to reduce the time consumption. Experiments demonstrate that EBGS can decrease the computing overhead for the three parts of BGS process simultaneously and achieve real-time performance and satisfactory detection accuracy under challenging environments.

中文翻译：

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通用嵌入式设备上基于全流程优化的运动物体检测背景扣除


实时计算机视觉任务正在消费电子领域兴起，具有轻量级的计算性能，是平衡计算效率和精度的一门精湛的设计艺术。在本文中，我们提出了嵌入式背景扣除（EBGS）——一种针对整个过程的优化算法，可同时提高计算效率和检测精度。 EBGS 利用简单高效的加法增加乘法减少 (AIMD) 滤波器来提高前景检测精度，而无需花费太多时间。此外，提出了收缩码本背景扣除（BGS）模型和随机模型更新之间的设计组合，以减少时间消耗。实验表明，EBGS可以同时降低BGS过程三个部分的计算开销，并在具有挑战性的环境下实现实时性能和令人满意的检测精度。