论文标题
跟踪系统的实时资源分配
Real-Time Resource Allocation for Tracking Systems
论文作者
论文摘要
自动跟踪是许多计算机视觉应用程序的关键。但是，由于检测人员的高计算成本，尤其是在超高分辨率图像中，许多跟踪系统都难以实时执行。我们提出了一种称为\ emph {partimax}的新算法，该算法仅通过将人检测器应用于图像的相关部分，从而大大降低了这一成本。 Partimax利用粒子过滤器中的信息选择图像中的$ n $候选\ emph {pixel box}的$ k $。我们证明，Partimax可以保证对与问题大小无关的误差范围进行近乎理想的选择。此外，现实生活数据集中的经验结果表明，我们的系统仅处理图像中的10 \％的像素盒，同时仍保留在处理所有像素盒时达到的原始跟踪性能的80 \％。
Automated tracking is key to many computer vision applications. However, many tracking systems struggle to perform in real-time due to the high computational cost of detecting people, especially in ultra high resolution images. We propose a new algorithm called \emph{PartiMax} that greatly reduces this cost by applying the person detector only to the relevant parts of the image. PartiMax exploits information in the particle filter to select $k$ of the $n$ candidate \emph{pixel boxes} in the image. We prove that PartiMax is guaranteed to make a near-optimal selection with error bounds that are independent of the problem size. Furthermore, empirical results on a real-life dataset show that our system runs in real-time by processing only 10\% of the pixel boxes in the image while still retaining 80\% of the original tracking performance achieved when processing all pixel boxes.
