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Adaptive 3D shape context representation for motion trajectory classification

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Abstract

The measurement of similarity between two motion trajectories is one of the fundamental task for motion analysis, perception and recognition. Previous research focus on 2D trajectory similarity measurement. With the advent of 3D sensors, it is possible to collect large amounts of 3D trajectory data for more precise motion representation. As trajectories in 3D space may often exhibit a similar motion pattern but may differ in location, orientation, scale, and appearance variations, the trajectory descriptor must be invariant to these degrees of freedom. Shape context is one of the rich local shape descriptors can be used to represent the trajectory in 2D space, however, rarely applied in the 3D motion trajectory recognition field. To handle 3D data, in this paper, we first naturally extend the shape context into the spatiotemporal domain by adopting a spherical neighborhood, and named it 3D Shape Context(3DSC). To achieve better global invariant on trajectories classification, the adaptive outer radius of 3DSC for extracting 3D Shape Context feature is proposed. The advantages of our proposed 3D shape context are: (1) It is invariant to motion trajectories translation and scale in the spatiotemporal domain; (2) It contains the whole trajectory points in the 3DSC ball volume, thus can achieve global information representation and is good for solving sub-trajectories problem; (3) It is insensitive to the appearance variations in the identical meaning trajectories, meanwhile, can greatly discriminate the distinct meaning trajectories. In trajectory recognition phase, we consider a feature-to-feature alignment between motion trajectories based on dynamic time warping and then use the one nearest neighbor (1NN) classifier for final accuracy evaluation. We test the performance of proposed 3D SC-DTW on UCI ASL large dataset, Digital hand dataset and the experimental results demonstrate the effectiveness of our method.

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References

  1. Bashir FI, Khokhar AA, Schonfeld D (2007) Real-time motion trajectory-based indexing and retrieval of video sequences[J]. IEEE Trans Multimed 9(1):58–65

    Article  Google Scholar 

  2. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522

    Article  Google Scholar 

  3. Chen L, Yao H, Sun X (2012) Action retrieval based on generalized dynamic depth data matching[C]// Visual Communications and Image Processing. 1–4

  4. Cheng H, Dai Z, Liu Z (2013) Image-to-Class Dynamic Time Warping for 3D hand gesture recognition[C]// Multimedia and Expo (ICME), 2013 I.E. International Conference on. IEEE, 1–6

  5. DEBOOR C, Fix GJ (1973) Spline approximation by quasiinterpolants[J]. Journal of Approximation Theory 8(1):19–45

    Article  MathSciNet  MATH  Google Scholar 

  6. Ding H, Trajcevski G, Scheuermann P et al (2008) Querying and mining of time series data: experimental comparison of representations and distance measures[J]. Proc Vldb Endowment 1(2):1542–1552

    Article  Google Scholar 

  7. Doliotis P (2013) Viewpoint invariant gesture recognition and 3d hand pose estimation using RGB-D[J]

  8. Doliotis P, Stefan A, McMurrough C. et al (2011) “Comparing gesture recognition accuracy using color and depth information,” in Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 20–22,ACM

  9. Frome A, Huber D, Kolluri R et al (2004) Recognizing objects in range data using regional point descriptors.[J]. Lect Notes Comput Sci 3023:224–237

    Article  MATH  Google Scholar 

  10. Grundmann M, Meier F, Essa I (2008) 3D Shape Context and Distance Transform for action recognition[C]// Pattern Recognition, 2008. ICPR 2008. 19th International Conference on. IEEE, 1–4

  11. Kim I C, Chien S I. Analysis of 3D Hand Trajectory Gestures Using Stroke-Based Composite Hidden Markov Models.[J]. Applied Intelligence, 2001, 15(2):131-143

  12. Jonathan A, Athitsos V, Yuan Q, Sclaroff S (2009) A unified framework for gesture recognition and spatiotemporal gesture segmentation. IEEE Trans Pattern Anal Mach Intell 31(9):1685–1699

    Article  Google Scholar 

  13. Kaya H, Gündüz-Öğüdücü Ş (2015) A distance based time series classification framework[J]. Inf Syst 51:27–42

    Article  Google Scholar 

  14. Li Z, Horain P, Pez A, et al (2009) Statistical Gesture Models for 3D Motion Capture from a Library of Gestures with Variants[C]// International Conference on Gesture in Embodied Communication and Human-Computer Interaction. Springer-Verlag, 219–230

  15. Liang B, Zheng L (2015) A Survey on Human Action Recognition Using Depth Sensors[C]// International Conference on Digital Image Computing: Techniques and Applications. IEEE, 1019–1029

  16. Lin WY, Hsieh CY (2013) Kernel-based representation for 2D/3D motion trajectory retrieval and classification[J]. Pattern Recogn 46(3):662–670

    Article  Google Scholar 

  17. Liu W, Fan Y, Li Z et al (2015) RGBD video based human hand trajectory tracking and gesture recognition system [J]. Math Probl Eng 2015:1–15

    Google Scholar 

  18. Liu W, Fan Y, Lei T, Zhang Z (2014) Human gesture recognition using orientation segmentation feature on random Forest. In Proceedings of IEEE China Summit & International Conference on Signal and Information Processing (SIP’14), pp. 480–484, Xi’an, China, July

  19. Lu G, Zhou Y, Li X et al (2016) Efficient action recognition via local position offset of 3D skeletal body joints[J]. Multimed Tools & Appl 75(6):3479–3494

    Article  Google Scholar 

  20. Niu W, Long J, Han D et al (2004) Human activity detection and recognition for video surveillance[C]. IEEE Int Conf Multimed Expo 1:719–722

    Google Scholar 

  21. Psarrou A, Gong S, Walter M (2002) Recognition of human gestures and behavior based on motion trajectories. Image Vis Comput 20(5–6):349–358

    Article  Google Scholar 

  22. Suzuki N, Hirasawa K, Tanaka K, et al (2010) Learning motion patterns and anomaly detection by Human trajectory analysis[C]// IEEE International Conference on Systems, Man and Cybernetics. IEEE, 498–503

  23. Tombari F, Salti S, Stefano LD (2010) Unique shape context for 3d data description[C]// Proceedings of the ACM workshop on 3D object retrieval. ACM, 57–62

  24. UCI KDD ASL Archive, http://kdd.ics.uci.edu/databases/auslan2/auslan.html

  25. Vlachos M, Gunopulos D, Das G (2004) Rotation invariant distance measures for trajectories[J]. Proceedings of Sigkdd, 707-712

  26. Wu SD, Li YF (2009) Flexible signature descriptions for adaptive motion trajectory representation, perception and recognition. Pattern Recogn 42(1):194–214

    Article  MATH  Google Scholar 

  27. Xiao D, Zahra D, Bourgeat P et al (2010) An improved 3D shape context based non-rigid registration method and its application to small animal skeletons registration [J]. Comput Med Imaging & Graphics Off J Comput Med Imaging Soc 34(4):321–32

    Article  Google Scholar 

  28. Xing Z, Pei J, Keogh E (2010) A brief survey on sequence classification[J]. Acm Sigkdd Explor Newslett 12(1):40–48

    Article  Google Scholar 

  29. Yang JY, Li YF and Wang KY (2010) “Mixed signature descriptor with global invariants for 3D motion trajectory perception and recognition,” in Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1952–1956

  30. Yang J, Li Y F, Wang K (2010) A new descriptor for 3D trajectory recognition via modified CDTW[C]// Automation and Logistics (ICAL), 2010 I.E. International Conference on. IEEE, 37–42.

  31. Yang J, Li Y F, Wang K (2011) Invariant trajectory indexing for real time 3D motion recognition[C]// IEEE/RSJ International Conference on Intelligent Robots & Systems IEEE/RSJ International Conference on Intelligent Robots & Systems, 3440–3445

  32. Yang J et al (2012) Mixed signature: an invariant descriptor for 3D motion trajectory perception and recognition [J]. Math Probl Eng 2012(1):488–488

    MathSciNet  Google Scholar 

  33. Yuting Su1, Haiyi Wang1, Peiguang Jing1, Chuanzhong Xu. A spatial-temporal iterative tensor decomposition technique for action and gesture recognition[J]

  34. Zhang Z, Tang P, Duan R (2015) Dynamic time warping under pointwise shape context[J]. Inf Sci 315:88–101

    Article  MathSciNet  Google Scholar 

  35. Zhao S, Chen L, Yao H et al (2015) Strategy for dynamic 3D depth data matching towards robust action retrieval[J]. Neurocomputing 151:533–543

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by National Natural Science Foundation of China (Grant no. 61501456), “Light of West China” Program of Chinese Academy of Sciences.

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Authors and Affiliations

  1. Key Laboratory of Spectral Imaging Technology, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China

    Weihua Liu & Geng Zhang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China

    Zuhe Li

  3. The University of Texas at Arlington, Texas, USA

    Zhong Zhang

Authors
  1. Weihua Liu
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  2. Zuhe Li
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  3. Geng Zhang
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  4. Zhong Zhang
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Correspondence to Weihua Liu.

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Liu, W., Li, Z., Zhang, G. et al. Adaptive 3D shape context representation for motion trajectory classification. Multimed Tools Appl 76, 15413–15434 (2017). https://doi.org/10.1007/s11042-016-3841-0

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  • DOI: https://doi.org/10.1007/s11042-016-3841-0

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