联结CPG(connectionist central pattern generator,CCPG)模型适于控制机器人生成步态,但是传统的CCPG模型无法很好地生成3维步态.为此,本文根据生物学原理,提出了一个改进的神经元模型和一个改进的层次化CCPG(hierarchical CCPG,HCCPG)模型.HCCPG模型能够生成相位协调的多自由度运动控制信号,从而解决了传统CCPG模型的步态生成问题.基于该模型,提出了一个统一方法来生成机器人的2维、3维步态.对转弯步态的特性进行了系统化深入分析,以便更好地利用该步态来适应狭窄的弯道环境.本文提出的HCCPG模型以及得到的步态特性,有助于提高机器人的环境适应能力.
In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve （AUC） value at the same time, and offers a discriminative and stable recognition performance.
