密集遮挡条件下的步态识别

Gait recognition algorithm in dense occlusion scene

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。

Gait recognition algorithms mainly rely on the contour sequence of pedestrian targets for feature extraction and recognition. In practical applications, pedestrians walk together, and the contour is easily occluded and interfered by other pedestrians, which significantly reduces the accuracy of gait recognition algorithm. To improve the robustness of gait recognition algorithm in dense occlusion scene, a deep-learning gait recognition algorithm based on unordered contour sequences is proposed. First, a simulation is conducted based on the Casia-B dataset, and the target contour simulation dataset for dense occlusion scene is established to verify the occlusion robustness of the algorithm. Second, a data augmentation method based on random binary expansion is proposed. However, owing to the limitations of horizontal pyramid pooling(HPP) structure in the area of gait recognition demonstrated through theory and experiment, a degenerated horizontal pyramid pooling(DHPP) structure is proposed. By combining the DHPP structure, CoordConv method, joint training, and pruning method, the perception ability of absolute position information in deep-learning features can be enhanced and the robustness of the algorithm for occlusion scene can be improved. In addition, the feature expression dimension of the target is reduced. The experimental results indicate that the proposed method is effective in improving the robustness of gait recognition algorithm.