Multi-camera person identification based on hidden markov model

doi:10.11772/j.issn.1001-9081.2014.06.1746

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (6): 1746-1752.DOI: 10.11772/j.issn.1001-9081.2014.06.1746

Previous Articles Next Articles

Multi-camera person identification based on hidden markov model

GAO Peng¹,GUO Lijun¹,ZHU Yiwei²,ZHANG Rong¹

1. College of Information Science and Engineering Ningbo University, Ningbo Zhejiang 315211, China;
2. Ningbo Power Supply Company, State Grid Zhejiang Corporation, Ningbo Zhejiang 315099, China

Received:2013-12-04 Revised:2014-01-28 Online:2014-07-02 Published:2014-06-01
Contact: GAO Peng
Supported by:
National Natural Science Foundation

基于隐马尔可夫模型的多摄像头人体对象的目标识别

高鹏¹,郭立君¹,朱一卫²,张荣¹

1. 宁波大学信息科学与工程学院,浙江宁波 315211;
2. 国家电网浙江省电力公司宁波供电公司,浙江宁波 315099

通讯作者: 高鹏
作者简介:高鹏(1989-),男,湖北枣阳人,硕士研究生,主要研究方向:计算机视觉;郭立君(1970-),男,辽宁凌源人,副教授,博士,主要研究方向: 计算机视觉、模式识别;朱一卫(1970-),男,浙江诸暨人,工程师,主要研究方向:模式识别、电网优化;张荣(1970-),女,河南鹤壁人,副教授,博士研究生,主要研究方向:数字图像取证、模式识别。
基金资助:
重庆市科委基础与前沿研究项目;浙江省新一代移动互联网用户端软件科技创新团队项目;宁波大学人才工程项目;宁波大学人才工程项目

Abstract

Abstract:

In the non-overlapping filed of multi-camera system, the single-shot person identification methods cannot well deal with appearance and viewpoint changes. Based on the multiple frames acquired from surveillance cameras, a new technique which combined Hidden Markov Model (HMM) with appearance-based feature was proposed. First, considering the structural constraint of human body, the whole-body appearance of each individual was equally vertically divided into sub-images. Then multi-level threshold method was used to extract Segment Representative Color (SRC) and Segment Standard Variation (SSV) feature. The feature dataset acquired from multiple frames was applied to train continuous density HMM,and the final recognition was realized by these well-trained model. Extensive experiments on two public datasets show that the proposed method achieves high recognition rate, improves robustness against viewpoint changes and low resolution, and it is simple and easy to realize.

摘要：

在非重叠多摄像机系统的人体对象目标识别中,针对基于单幅图片的识别算法不能较好处理对象表观和视角变化的问题,提出基于人体图像序列的算法。该算法用隐马尔可夫模型(HMM)融合多幅图片的特征,先考虑人体结构的约束,将人体图像在垂直方向上划分为多个相等的图像区域;然后采用多层阈值分割算法提取区域代表性颜色特征(SRC)和标准差特征(SSV);再用每个人体对象的多幅图片提取的特征数据集训练该对象的连续密度HMM;最后利用训练的模型实现人体对象的目标识别。该方法在两个公开数据集上进行的实验都获得了较高的识别率,提高了对摄像头视角变化、低分辨率的鲁棒性,且简单易实现。

CLC Number:

TP391.4
TP37

GAO Peng GUO Lijun ZHU Yiwei ZHANG Rong. Multi-camera person identification based on hidden markov model[J]. Journal of Computer Applications, 2014, 34(6): 1746-1752.

高鹏郭立君朱一卫张荣. 基于隐马尔可夫模型的多摄像头人体对象的目标识别[J]. 计算机应用, 2014, 34(6): 1746-1752.

References

[1]WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[2]KALE A, SUNDARESAN A, RAJAGOPALAN A N, et al. Identification of humans using gait [J]. IEEE Transactions on Image Processing, 2004, 13(9):1163-1173.
[3]ROY A, SURAL S, MUKHERJEE J. A hierarchical method combining gait and phase of motion with spatiotemporal model for person re-identification [J]. Pattern Recognition Letters, 2012, 33(14):1891-1901.
[4]RABINER L R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of the IEEE, 1989, 77(2):257-286.
[5]ZHENG W-S, GONG S, XIANG T. Person re-identification by probabilistic relative distance comparison[C]// Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2011:649-656.
[6]GRAY D, TAO H. Viewpoint invariant pedestrian recognition with an ensemble of localized features[C]// Proceedings of the 10th European Conference on Computer Vision, LNCS 5302. Berlin: Springer-Verlag, 2008: 262-275.
[7]PROSSER B, ZHENG W S, GONG S G, et al. Person re-identification by support vector ranking[C]// Proceedings of the 2010 British Machine Vision Conference. Edinburgh: BMVA Press, 2010: 21.1-21.11.
[8]FARENZENA M, BAZZANI L, PERINA A, et al. Person re-identification by symmetry-driven accumulation of local features[C]// Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2010:2360-2367.
[9]ZHAO R, OUYANG W, WANG X G. Unsupervised salience learning for person re-identification[C]// Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2013:3586-3593.
[10]KAI J, ARENS M. View-invariant person re-identification with an implicit shape model [C]// Proceedings of the 2011 IEEE International Conference on Advanced Video and Signal-based Surveillance. Piscataway: IEEE Press, 2011: 197-202.
[11]EISENBACH M, KOLAROW A, SCHENK K, et al. View invariant appearance-based person reidentification using fast online feature selection and score level fusion[C]// Proceedings of the IEEE 9th International Conference on Advanced Video and Signal-based Surveillance. Piscataway: IEEE Press,2012:184-190.
[12]KATO J, WATANABE T, JOGA S, et al. An HMM-based segmentation method for traffic monitoring movies [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(9): 1291-1296.
[13]LEE J M, KIM S J, HWANG Y, et al. Diagnosis of mechanical fault signals using continuous hidden Markov model[J]. Journal of Sound and Vibration, 2004,276(3/4/5): 1065-1080.
[14]BICEGO M, CASTELLANI U, MURINO V. A hidden Markov model approach for appearance-based 3D object recognition [J]. Pattern Recognition Letters, 2005, 26(16): 2588-2599.
[15]BISWAS S. Segmentation based compression for gray level images[J].Pattern Recognition, 2003, 36(7): 1501-1517.
[16]ARORA S, ACHARYA J, VERMA A, et al. Multilevel thresholding for image segmentation through a fast statistical recursive algorithm[J]. Pattern Recognition Letters, 2008,29(2): 119-125.
[17]OTSU N. A threshold selection method from gray-level histograms [J]. IEEE Transactions on System, Man, and Cybernetics, 1979, 9(1): 62-66.
[18]BIRCHFIELD S T, RANGARAJAN S. Spatiograms versus histograms for region-based tracking[C]// Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2005, 2:1158-1163.
[19]CONG D N T, KHOUDOUR L, ACHARD C, et al. People re-identification by means of a camera network using a graph based approach[C]// MVA2009: IAPR Conference on Machine Vision Applications. Tokyo: MVA Conference Committee, 2009: 152-155.
[20]BUCHSBAUM G. A spatial processor model for object color perception[J]. Journal of the Franklin Institute, 1980,310(1): 1-26.
[21]YANG H H, MOODY J. Feature selection based on joint mutual information[C]// Proceedings of the 1999 International ICSC Symposium on Advances in Intelligent Data Analysis. Berlin: Springer-Verlag, 1999: 22-25.
[22]BALTIERI D, VEZZANI R, CUCCHIARA R. 3DPes: 3D people dataset for surveillance and forensics[C]// Proceedings of the 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding. New York: ACM Press, 2011:59-64.
[23]SCHWARTZ W R, DAVIS L S. Learning discriminative appearance-based models using partial least squares[C]// Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing. Berlin: Springer-Verlag, 2009: 322-329.

Multi-camera person identification based on hidden markov model

基于隐马尔可夫模型的多摄像头人体对象的目标识别

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	. Pavement defect detection algorithm with enhanced morphological perception [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	YANG Jianfeng, CHEN Bin, LI Yuxuan. Self-supervised point cloud anomaly detection method based on point cloud reconstruction [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	. Binocular vision object location algorithm for robot arm grasping [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Multi-attention contrastive learning for infrared small target detection [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	LI Zhonghua, ZHONG Gengxin, FAN Ping, ZHU Hengliang. Camouflaged object detection by boundary mining and background guidance [J]. Journal of Computer Applications, 0, (): 0-0.
[6]	. Multi-view difficult airway recognition based on discriminant region guidance [J]. Journal of Computer Applications, 0, (): 0-0.
[7]	. Privacy preserving localization of surveillance images based on large vision models [J]. Journal of Computer Applications, 0, (): 0-0.
[8]	. Data augmentation method for abnormal passenger behavior in elevators based on dynamic graph convolutional network [J]. Journal of Computer Applications, 0, (): 0-0.
[9]	. Text-based person retrieval method based on multi-granularity shared semantic center association [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	. Distribution adaptation and dynamic curriculum pseudo-labeling framework for semi-supervised fire detection [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	. Image Caption Method Based on Swin-Transformer and Multi-Scale Feature Fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	Keyi FU, Gaocai WANG, Man WU. Few-shot object detection method based on improved region proposal network and feature aggregation [J]. Journal of Computer Applications, 2024, 44(12): 3790-3797.
[13]	Chenghao YANG, Jie HU, Hongjun WANG, Bo PENG. Incomplete multi-view clustering algorithm based on attention mechanism [J]. Journal of Computer Applications, 2024, 44(12): 3784-3789.
[14]	Peng FANG, Fan ZHAO, Baoquan WANG, Yi WANG, Tonghai JIANG. Development， technologies and applications of blockchain 3.0 [J]. Journal of Computer Applications, 2024, 44(12): 3647-3657.
[15]	Yudong PANG, Zhixing LI, Weijie LIU, Tianhao LI, Ningning WANG. Small target detection model in overlooking scenes on tower cranes based on improved real-time detection Transformer [J]. Journal of Computer Applications, 2024, 44(12): 3922-3929.