摘要
针对可变形部件模型中特征金字塔需要精细计算,从而导致计算速度较慢这一问题,提出一种结合快速特征金字塔的可变形部件模型对象检测算法.首先在特征金字塔中根据间隔选取若干个基准尺度进行精确计算,然后依据多尺度特征中的幂指定律,近似计算得到多分辨率的图像特征.算法采用尺度上稀疏采样的特征金字塔来外推精细采样的特征金字塔,然后采用预先训练好的模板进行类别检测,得出检测结果.在PASCAL VOC2007以及INRIA数据集上的实验结果表明,该算法可以明显加速模型中特征计算的速度,而检测精度仅略有下降.
To solve the problem that feature pyramids of deformable part models being computed explicitly lead to slowcomputation,the paper proposed an object detection algorithm based on deformable part models with fast computation of feature pyramids. Firstly we select and compute explicitly several benchmark scales based on scale intervals on feature pyramids,then approximately compute multi-resolution image features according to the multi-scale power law. We utilize sparsely-sampled feature pyramids on the scale to approximate the finely-sampled feature pyramids,and then use pre-trained templates to detect the category to obtain final detection results. The experimental results on Pascal VOC 2007 dataset and INRIA dataset showthat the algorithm in the paper apparently accelerates the speed of feature computation in the model with negligible loss in detection accuracy.
引文
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