Human Carrying Status in Visual Surveillance
Human Carrying Status in Visual Surveillance
复制标题
DOI：
10.1109/cvpr.2006.138
复制
发表时间：
2006-06
期刊：
影响因子：
--
通讯作者：
D. Tao;Xuelong Li;S. Maybank;Xindong Wu
中科院分区：
文献类型：
--
作者：
D. Tao;Xuelong Li;S. Maybank;Xindong Wu
当一个人背着包、手提箱或帆布背包等物品时，他或她的步态会发生变化。结果，由于平均步态图像过于简单而无法表示携带状态，给人体识别和跟踪增加了难度。因此，在本文中，我们首先引入了一套基于Gabor的人类步态外观模型，因为Gabor功能类似于哺乳动物皮层简单细胞的感受野轮廓。特征空间的高维使得训练变得困难。为了解决这一问题，我们提出了一种通用张量判别分析（GTDA），它无缝地将物体（基于Gabor的人类步态外观模型）的结构信息作为自然约束。GTDA与以往基于张量的判别分析方法的不同之处在于训练是收敛的。现有的方法在训练阶段无法收敛。这使得它们不适合执行实际任务。在USF基线数据集上进行实验，从步态轮廓中识别人的身份。结合GTDA的Gabor步态被证明明显优于现有的基于外观的方法。
A person’s gait changes when he or she is carrying an object such as a bag, suitcase or rucksack. As a result, human identification and tracking are made more difficult because the averaged gait image is too simple to represent the carrying status. Therefore, in this paper we first introduce a set of Gabor based human gait appearance models, because Gabor functions are similar to the receptive field profiles in the mammalian cortical simple cells. The very high dimensionality of the feature space makes training difficult. In order to solve this problem we propose a general tensor discriminant analysis (GTDA), which seamlessly incorporates the object (Gabor based human gait appearance model) structure information as a natural constraint. GTDA differs from the previous tensor based discriminant analysis methods in that the training converges. Existing methods fail to converge in the training stage. This makes them unsuitable for practical tasks. Experiments are carried out on the USF baseline data set to recognize a human’s ID from the gait silhouette. The proposed Gabor gait incorporated with GTDA is demonstrated to significantly outperform the existing appearance-based methods.