The HumanID gait challenge problem: Data sets, performance, and analysis
The HumanID gait challenge problem: Data sets, performance, and analysis
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DOI：
10.1109/tpami.2005.39
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发表时间：
2005-02-01
影响因子：
23.6
通讯作者：
Bowyer, KW
中科院分区：
文献类型：
--
作者：
Sarkar, S;Phillips, PJ;Bowyer, KW
通过分析从视频中提取的步态模式来识别人是近年来的一个热门研究课题。然而，问题“可解决”的条件没有得到理解或定性。为了提供一种测量步态识别的进展和特性的方法，我们引入了HumanID步态挑战问题。挑战问题包括一个基线算法，一组12个实验和一个大型数据集。基线算法通过背景减除估计轮廓，并通过轮廓的时间相关性进行识别。这12个实验的难度越来越大，正如基线算法所测量的那样，并检查了五个协变量对性能的影响。协变量为：观察角度的改变、鞋型的改变、行走表面的改变、携带或不携带公文包以及所比较的序列之间的经过时间。12个实验的识别率从最简单的78%到最难的3%不等。所有五个协变量对性能都有统计学显著影响，其中步行表面和时间差的影响最大。该数据集由来自122名受试者的1，870个序列组成，跨越5个协变量（1.2 MB数据）。步态数据、基线算法的源代码以及运行、评分和分析挑战实验的脚本可在http://www.GaitChallenge.org上获得。该基础设施支持步态识别算法的进一步开发和其他实验，以了解新算法的优势和劣势。呈现的实验结果越详细，可能的荟萃分析就越详细，理解就越深入。正是这种潜力，从采用这个挑战性的问题，代表了从传统的计算机视觉研究方法的根本出发。
Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the HumanID Gait Challenge Problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 Gigabytes of data). The gait data, the source code of the baseline algorithm, and scripts to run, score, and analyze the challenge experiments are available at http://www.GaitChallenge.org. This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The more detailed the experimental results presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology.