密集遮挡条件下的步态识别

步态识别算法主要依赖行人目标的时序轮廓进行特征提取和判别。在实际应用中行人具有结伴行走的特点,轮廓提取易受到其他行人的遮挡和干扰,大幅降低了步态识别算法的精度。为提高人员密集遮挡严重的场景下步态识别算法的鲁棒性,提出一种基于无序序列的深度步态识别算法。首先在Casia-B数据集的基础上进行仿真,建立遮挡情况下的目标轮廓仿真数据集,用于对算法进行遮挡鲁棒性验证;其次,提出基于随机二值膨胀的数据增广方法,同时通过理论和实验论证了HPP(Horizontal Pyramid Pooling)结构在步态识别问题中的局限性,提出退化水平金字塔结构DHPP,利用DHPP结构、CoordConv方法和联合训练裁剪方法的配合,在深度特征中增强绝对位置信息的感知能力,提升算法遮挡鲁棒性的同时减少目标特征表达维度。实验结果表明,所提方法对于步态识别的鲁棒性提升效果明显。

Creation of Extension Knowledge Base System About Intelligent Detection in Dendrobium Huoshanense Photosynthesis Process

Aiming at the limitations of the existing knowledge representations in intelligent detection,a novel extension-based knowledge representation(EKR) is proposed.The definitions,grammar rules,and formal semantics of EKR are presented.A rhombus solving strategy(RSS) based on EKR is discussed in detail,including creation of the problem oriented model,extension operator,the solution formation of contradictions problem and extended inference of matter-element.A knowledge base system based on EKR and RSS is developed,which is applied in intelligent detection in the Dendrobium huoshanense photosynthesis process(DHPP).More reasonable results are obtained than traditional rule-based system.The EKR is feasible in intelligent detection to solve the limitations of traditional knowledge representations.