基于蚁群算法和遗传算法的步态识别研究
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摘要
在计算机视觉和智能视频监控领域,步态识别是一个新兴的研究方向,它是根据人们走路的方式来进行人的身份识别。步态的分析与识别在安全领域、人机交互、动画、虚拟现实和医学等诸多领域有着重要的应用前景和巨大的经济价值。随着计算机信息技术的飞速发展,步态自动识别研究已经取得了很大的进展。为了提高识别的准确性,大多数研究都对步态特征的提取给予了较大的关注,而忽视了对识别算法的研究。本文结合国家自然科学基金资助项目,主要针对多种识别算法对步态特征识别性能的影响进行了探索性的研究。
论文首先简要综述了步态识别技术,评述了在步态识别领域的一些主流方法和途径,讨论了步态识别应用的理论和方法,并对现有的识别技术进行了概括和总结。其次从运动检测、感兴趣区域提取与处理、步态周期分割和步态能量图几个方面介绍了步态特征的提取过程。针对动态背景下的运动检测,使用混合高斯模型进行背景建模,大大提高了检测出来的步态特征的精度。在感兴趣区域提取与处理部分,采用全局搜索方法寻找最小人体矩形框,并对其进行归一化和中心化处理。在检测步态周期时,转化为求取步态序列信号自相关函数的周期。根据得到的步态序列构建步态能量图作为识别特征。
本文的核心内容在于对多种识别技术的比较研究:提出一种基于HMM的步态识别算法,使用HMM中的Baum-Welch算法对每个人体步态建模,然后使用前向.后向算法进行识别,用这种方法的识别率可以达到75%以上;首次把蚁群算法应用到步态识别中,该算法模拟了蚂蚁寻找食物的自适应过程,能够对输入的样本自动训练出聚类中心,这样就省去人工干预训练样本的过程,并且该方法的识别率较HMM识别算法提高了五个百分点;用遗传算法对蚁群算法进行优化,把每个蚂蚁编码成一个染色体,通过染色体适应度的大小进行选择淘汰操作,并且通过交叉概率和变异概率进行杂交和变异运算,实验结果表明该方法的正确识别率在90%以上。
最后,在CASIA数据库对三种不同识别算法进行了大量的实验,并对实验结果进行了比较和分析,总结了多种识别技术在识别的有效性和计算复杂度等方面的优缺点,并形成结论:选择合适的识别算法可以大大提高步态特征的正确识别率。另外,本文提出的基于蚁群的识别算法还具有很强的抗干扰能力,解决了一般步态识别系统在这一方面的缺陷。本论文提出的新算法在很大程度上提高了图像的处理效果、分类结果和运行速度,实验结果是令人满意的。本文的工作为如何选择合适的识别技术以达到最佳识别效果提供了重要的参考依据。
In computer vision and intelligent video surveillance system, gait recognition is a new research direction, which is to identify people based on the way people walk. Gait analysis and identification, which has important potential future applications in fields such as security, machine interaction, computer animation, virtual reality and medicine, is of great economic value. With the development of computer and information technology, gait identification technology by computer has made great progress. In order to increase the accuracy of gait recognition result, most researchers focused on the research of extracting gait feature and neglected the research on recognition algorithms. In this paper, based on the National Natural Science Foundation-funded projects, we mainly study the impact of a variety of gait recognition algorithms to identification performance.
First, the paper gives a brief overview of gait recognition technology. It reviews several popular gait recognition algorithms, discusses the application of gait recognition theory and method, and summarizes the existing recognition technology. Then, the paper introduces gait feature extraction technology based on the discussion on motion detection, interesting region extraction and processing, gait cycle detection and gait energy images. For motion detection in dynamic scenes, binary motion images are obtained with mixture Gaussian background modeling. For interesting region extraction and processing, traversing search method is used to find the smallest human rectangular box. Then the human rectangular box is regularized and centered. In gait cycle detection, we get the gait cycle by calculating the auto-correlation function of the gait signal. Gait energy images, which are constructed from gait sequences, are used as classification features.
The core content of this paper is to do research in a variety of recognition technologies and compare the performance of them: A gait recognition algorithm based on HMM is proposed. Baum-Welch algorithm is used to model the gait character of every body; Then Forward-Backward algorithm is used to recognize gait character of different bodies. The correct recognition rate of the algorithm based on HMM is more than 75%. It is the first time that ant colony algorithm is used in gait recognition. The algorithm simulates the adaptive process that ants find food, and can find the cluster center automatically based on the training of samples. The correct recognition rate of the algorithm enhances five percent than that of the algorithm based on HMM. We also use genetic algorithm to optimize the clustering center of ant colony algorithm. Every ant is coded as a chromosome. Then we choose or lose ants based on the fitness degree of the chromosome, and calculate the crossover probability and mutation probability. Experimental results show that the correct recognition rate of the method is more than 90%.
Finally, with a large amount of experiments on CASIA database, we compare the recognition results of these three approaches and summarize the strengths and weaknesses of them on effectiveness and computational complexity. We find that the proper recognition algorithm can improve recognition result obviously and the algorithms proposed in the paper have high capability of anti-jamming which is lacked by general gait identification systems. Experiments show that the new proposed algorithms, which can enhance the quality of gait image efficiently, improve the recognition results and run faster, meet our expectation. The work of this paper provides an important reference for how to choose suitable technologies to achieve the best recognition results.
论文首先简要综述了步态识别技术,评述了在步态识别领域的一些主流方法和途径,讨论了步态识别应用的理论和方法,并对现有的识别技术进行了概括和总结。其次从运动检测、感兴趣区域提取与处理、步态周期分割和步态能量图几个方面介绍了步态特征的提取过程。针对动态背景下的运动检测,使用混合高斯模型进行背景建模,大大提高了检测出来的步态特征的精度。在感兴趣区域提取与处理部分,采用全局搜索方法寻找最小人体矩形框,并对其进行归一化和中心化处理。在检测步态周期时,转化为求取步态序列信号自相关函数的周期。根据得到的步态序列构建步态能量图作为识别特征。
本文的核心内容在于对多种识别技术的比较研究:提出一种基于HMM的步态识别算法,使用HMM中的Baum-Welch算法对每个人体步态建模,然后使用前向.后向算法进行识别,用这种方法的识别率可以达到75%以上;首次把蚁群算法应用到步态识别中,该算法模拟了蚂蚁寻找食物的自适应过程,能够对输入的样本自动训练出聚类中心,这样就省去人工干预训练样本的过程,并且该方法的识别率较HMM识别算法提高了五个百分点;用遗传算法对蚁群算法进行优化,把每个蚂蚁编码成一个染色体,通过染色体适应度的大小进行选择淘汰操作,并且通过交叉概率和变异概率进行杂交和变异运算,实验结果表明该方法的正确识别率在90%以上。
最后,在CASIA数据库对三种不同识别算法进行了大量的实验,并对实验结果进行了比较和分析,总结了多种识别技术在识别的有效性和计算复杂度等方面的优缺点,并形成结论:选择合适的识别算法可以大大提高步态特征的正确识别率。另外,本文提出的基于蚁群的识别算法还具有很强的抗干扰能力,解决了一般步态识别系统在这一方面的缺陷。本论文提出的新算法在很大程度上提高了图像的处理效果、分类结果和运行速度,实验结果是令人满意的。本文的工作为如何选择合适的识别技术以达到最佳识别效果提供了重要的参考依据。
In computer vision and intelligent video surveillance system, gait recognition is a new research direction, which is to identify people based on the way people walk. Gait analysis and identification, which has important potential future applications in fields such as security, machine interaction, computer animation, virtual reality and medicine, is of great economic value. With the development of computer and information technology, gait identification technology by computer has made great progress. In order to increase the accuracy of gait recognition result, most researchers focused on the research of extracting gait feature and neglected the research on recognition algorithms. In this paper, based on the National Natural Science Foundation-funded projects, we mainly study the impact of a variety of gait recognition algorithms to identification performance.
First, the paper gives a brief overview of gait recognition technology. It reviews several popular gait recognition algorithms, discusses the application of gait recognition theory and method, and summarizes the existing recognition technology. Then, the paper introduces gait feature extraction technology based on the discussion on motion detection, interesting region extraction and processing, gait cycle detection and gait energy images. For motion detection in dynamic scenes, binary motion images are obtained with mixture Gaussian background modeling. For interesting region extraction and processing, traversing search method is used to find the smallest human rectangular box. Then the human rectangular box is regularized and centered. In gait cycle detection, we get the gait cycle by calculating the auto-correlation function of the gait signal. Gait energy images, which are constructed from gait sequences, are used as classification features.
The core content of this paper is to do research in a variety of recognition technologies and compare the performance of them: A gait recognition algorithm based on HMM is proposed. Baum-Welch algorithm is used to model the gait character of every body; Then Forward-Backward algorithm is used to recognize gait character of different bodies. The correct recognition rate of the algorithm based on HMM is more than 75%. It is the first time that ant colony algorithm is used in gait recognition. The algorithm simulates the adaptive process that ants find food, and can find the cluster center automatically based on the training of samples. The correct recognition rate of the algorithm enhances five percent than that of the algorithm based on HMM. We also use genetic algorithm to optimize the clustering center of ant colony algorithm. Every ant is coded as a chromosome. Then we choose or lose ants based on the fitness degree of the chromosome, and calculate the crossover probability and mutation probability. Experimental results show that the correct recognition rate of the method is more than 90%.
Finally, with a large amount of experiments on CASIA database, we compare the recognition results of these three approaches and summarize the strengths and weaknesses of them on effectiveness and computational complexity. We find that the proper recognition algorithm can improve recognition result obviously and the algorithms proposed in the paper have high capability of anti-jamming which is lacked by general gait identification systems. Experiments show that the new proposed algorithms, which can enhance the quality of gait image efficiently, improve the recognition results and run faster, meet our expectation. The work of this paper provides an important reference for how to choose suitable technologies to achieve the best recognition results.
引文
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[2]Lee Chan-su,A.Elgammal.Gait style and gait content:bilinear models for gait recognition using gait re-sampling.In:Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition,2004:147-152
[3]L.Lee,W.E.L.Grimson.Gait analysis for recognition and classification.In:Fifth IEEE International Conference on Automatic Face and Gesture Recognition,2002:148-155
[4]C.Bauckhage,J.K.Tsotsos,F.E.Bunn.Detecting Abnormal Gait.In:Proc.Canadian Conf.on Computer and Robot Vision,IEEE,2005:282-288
[5]Davrondzhon Gafurov.A Survey of Biometric Gait Recognition:Approaches,Security and Challenges.In Proceedings of Annual Norwegian Computer Science Conference,Tapir,2007:119-130
[6]D.Cunado,M.Nixon,J.Carter.Using gait as a biometric,via phase-weighted magnitude spectra.In:Proc International Conference on Audio- and Video-based Biometric Person Authentication,Crans-Montana,Switzerland,1997:95-102
[7]J.Little,J.Boyd.Recognizing people by their gait:the shape of motion.Journal of Computer Vision Research,1998,1(2):2-32
[8]Amos Y.Johnson and Aaron F.Bobick.A multi-view method for gait recognition using static body parameters.In:Third International Conference on Audio- and Video-Based Biometric Person Authentication,2001:301-311
[9]Zongyi Liu and Sudeep Sarkar.Simplest representation yet for gait recognition:Averaged silhouette.In:International Conference on Pattern Recognition,2004:211-214
[10]http://www.darpa.mil/ito/research/hid/index.html
[11]Rawesak Tanawongsuwan,Aaron Bobick.Gait recognition from time-normalized joint-angle trajectories in the walking plane.In:Proceedings of IEEE Computer Vision and Pattern Recognition Conference 2001: 726-731
[12] Rawesak, Tanawongsuwan, F. Aaron. Bobick.Performance Analysis of Time-Distance Gait Parameters under Different Speeds. AVBPA, 2003:715-724
[13] Rawesak Tanawongsuwan, Aaron F. Bobick: Modelling the Effects of Walking Speed on Appearance-Based Gait Recognition. CVPR 2004:783-790
[14] David Tolliver, Robert T. Collins.Gait Shape Estimation for Identification.In:AVBPA 2003: 734-742
[15] G. Shakhnarovich, T. Darrell.On Probabilistic Combination of Face and Gait Cues for Identification.In: Proceedings of the Int. Conf. on Automatic Face and Gesture Recognition, 2002:169-174
[16] G. Shakhnarovich, L. Lee, T. Darrell. Integrated face and gait recognition from multiple views. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2001: 439-446
[17] J. Little, J. Boyd. Recognizing people by their gait: the shape of motion. Journal of Computer Vision Research, 1998,1(2): 2-32
[18] A. K. Amit, P. C. Naresh, K. Volker, A. N. Rajagopalan. Gait-Based Recognition of Humans Using Continuous HMMs. FGR 2002: 336-341
[19] A. K. Amit, Amit K. Roy Chowdhury, Rama Chellappa. Towards a View Invariant Gait Recognition Algorithm. AVSS 2003: 143-150
[20] N. Cuntoor, A. Kale, R. Chellapps. Combining multiple evidences for gait recognition. In: Proceedings of the 2003 International Conference on Multimedia and Expo,2003:113-116
[21] D. Cunado, M. Nixon, J, Carter. Using gait as a biometric, via phase-weighted magnitude spectra. In: Proc International Conference on Audio- and Video-based Biometric Person Authentication, 1997: 95-102
[22] D. Cunado, M S. Nixon, J. N. Carter. Automatic Extraction and Description of Human Gait Models for Recognition Purposes. CVIU, 2003, 90 (1):1-41
[23] J. P. Foster, M. S. Nixon, A. P. Bennett. Automatic gait recognition using area-based metrics.Pattern Recognition Letters,2003,24:2489-2497
[24]Liang Wang,Huazhong Ning,Weiming Hu and Tieniu Tan.Gait Recognition Based on Procrustes Shape Analysis.In:IEEE International Conference on Image Processing(ICIP),2002:22-252002
[25]王亮,胡卫明,谭铁牛,基于步态的身份识别,计算机学报,2003,26(3):353-360
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