基于优化图的半监督学习的行人检测

Pedestrian Detection Based on Optimized Semi-Supervised Learning Method

摘要: 行人检测是当前机器视觉领域的挑战性课题之一。为了提高行人检测效率，提出一种基于优化图的半监督学习的行人检测算法。首先，提取每幅图像的形状上下文特征，并采用选择性搜索提取出行人候选区域建议框；然后，提出一种优化图的半监督学习方法，该方法融合包含行人的建议框之间距离尽量小，而不包含行人的建议框和包含行人的建议框之间的距离尽量大的先验知识构建模型，解决在行人检测过程中普遍存在训练数据不足，挖掘不到足够的先验知识，没有很好的泛化性问题；最后，将提出的算法与现有的行人检测方法进行实验比较，验证算法的有效性。

Abstract: Pedestrian detection remains one of the challenging tasks in the area of computer vision. In order to improve the effectiveness of pedestrian detection, this paper proposes a new approach to pedestrian detection. First, the shape context features of each image are represented. Then, we specifically design a novel optimized graph-based semi-supervised learning for pedestrian detection, in which we maximize the average weighed distance between the suggestion box with pedestrians and the suggestion box without pedestrians, and minimize the average weighed distance between the suggestion boxes with pedestrian. Training data insufficiency and lack of generalization of learning method can be resolved. Compared with several other approaches, the experimental results show that this approach performs more effectively and accurately.