IEEE Trans Image Process. 2013 Dec;22(12):4752-61. doi: 10.1109/TIP.2013.2277823. Epub 2013 Aug 8.
In pedestrian detection, as sophisticated feature descriptors are used for improving detection accuracy, its processing speed becomes a critical issue. In this paper, we propose a novel speed-up scheme based on multiple-instance pruning (MIP), one of the soft cascade methods, to enhance the processing speed of support vector machine (SVM) classifiers. Our scheme mainly consists of three steps. First, we regularly split an SVM classifier into multiple parts and build a cascade structure using them. Next, we rearrange the cascade structure for enhancing the rejection rate, and then train the rejection threshold of each stage composing the cascade structure using the MIP. To verify the validity of our scheme, we apply it to a pedestrian classifier using co-occurrence histograms of oriented gradients trained by an SVM, and experimental results show that the processing time for classification of the proposed scheme is as low as one-hundredth of the original classifier without sacrificing detection accuracy.
在行人检测中，为了提高检测精度，使用了复杂的特征描述符，因此其处理速度成为一个关键问题。在本文中，我们提出了一种基于多实例剪枝（MIP）的新的加速方案，这是一种软级联方法之一，用于提高支持向量机（SVM）分类器的处理速度。我们的方案主要包括三个步骤。首先，我们定期将 SVM 分类器分成多个部分，并使用它们构建级联结构。接下来，我们重新排列级联结构以提高拒绝率，然后使用 MIP 训练构成级联结构的每个阶段的拒绝阈值。为了验证我们方案的有效性，我们将其应用于使用 SVM 训练的方向梯度共生直方图的行人分类器，实验结果表明，分类的处理时间是原始分类器的百分之一，而不会牺牲检测精度。
