Pedestrian detection in the embedded system, such as video surveillance equipment, usually involves low-resolution pedestrian samples and requires a low computational cost. Many pedestrian detectors rely on a large feature pool and suffer in their efficiency and performance for real-time monitoring. In this paper, a set of light-weight features is proposed to enhance the pedestrian detection performance when a small-medium scale of training data with low-resolution images is available. To address this issue, a difference matrix projection (DMP) is developed to compute aggregated multi-oriented pixel differences using global matrix operations. Both the pixel differences and aggregation are computed using global matrix projection to avoid the laborious iterative operations. We tested our method on the INRIA, Daimler Chrysler classification (Daimler-CB), NICTA, and Caltech Pedestrian datasets. The experiments on these benchmark data sets show encouraging results in terms of detection performance, particularly for image datasets with low-resolution pedestrians.

中文翻译：

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使用像素差分矩阵投影的行人检测

嵌入式系统中的行人检测，如视频监控设备，通常涉及低分辨率的行人样本，需要较低的计算成本。许多行人检测器依赖于大型特征池，并且实时监控的效率和性能受到影响。在本文中，当具有低分辨率图像的中小规模训练数据可用时，提出了一组轻量级特征来增强行人检测性能。为了解决这个问题，开发了差分矩阵投影 (DMP) 以使用全局矩阵运算来计算聚合的多向像素差异。像素差异和聚合都是使用全局矩阵投影计算的，以避免费力的迭代操作。我们在 INRIA 上测试了我们的方法，Daimler Chrysler 分类 (Daimler-CB)、NICTA 和 Caltech Pedestrian 数据集。在这些基准数据集上的实验在检测性能方面显示出令人鼓舞的结果，特别是对于具有低分辨率行人的图像数据集。