Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Human gait recognition from motion capture data in signature poses

Human gait recognition from motion capture data in signature poses

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Most contribution to the field of structure-based human gait recognition has been done through design of extraordinary gait features. Many research groups that address this topic introduce a unique combination of gait features, select a couple of well-known object classifiers, and test some variations of their methods on their custom Kinect databases. For a practical system, it is not necessary to invent an ideal gait feature – there have been many good geometric features designed – but to smartly process the data there are at the authors’ disposal. This work proposes a gait recognition method without design of novel gait features; instead, the authors suggest an effective and highly efficient way of processing known types of features. Their method extracts a couple of joint angles from two signature poses within a gait cycle to form a gait pattern descriptor, and classifies the query subject by the baseline 1-NN classifier. Not only are these poses distinctive enough, they also rarely accommodate motion irregularities that would result in confusion of identities. They experimentally demonstrate that their gait recognition method outperforms other relevant methods in terms of recognition rate and computational complexity. Evaluations were performed on an experimental database that precisely simulates street-level video surveillance environment.

References

    1. 1)
      • 8. Ahmed, M., Al-Jawad, N., Sabir, A.: ‘Gait recognition based on Kinect sensor’. Proc. SPIE, Real-Time Image and Video Processing, 2014, vol. 9139, pp. B:1B:10.
    2. 2)
      • 9. Andersson, V., Dutra, R., Araujo, R.: ‘Anthropometric and human gait identification using skeleton data from Kinect sensor’. Proc. ACM Symp. Applied Computing, 2014, pp. 6061.
    3. 3)
      • 12. Dikovski, B., Madjarov, G., Gjorgjevikj, D.: ‘Evaluation of different feature sets for gait recognition using skeletal data from Kinect’. Information and Communication Technology, Electronics and Microelectronics, 2014, pp. 13041308.
    4. 4)
      • 11. Derlatka, M., Bogdan, M.: ‘Fusion of static and dynamic parameters at decision level in human gait recognition’. Pattern Recognition and Machine Intelligence, 2015 (LNCS, 9124), pp. 515524.
    5. 5)
      • 15. Krzeszowski, T., Switonski, A., Kwolek, B., et al: ‘DTW-based gait recognition from recovered 3-D joint angles and inter-ankle distance’, Comput. Vis. Graph., 2014, 8671, pp. 356363.
    6. 6)
      • 18. Preis, J., Kessel, M., Werner, M., et al: ‘Gait recognition with Kinect’. Int. Workshop on Kinect in Pervasive Computing, 2012.
    7. 7)
      • 16. Kumar, M.S.N., Babu, R.V.: ‘Human gait recognition using depth camera: a covariance based approach’. Computer Vision, Graphics and Image Processing (ICVGIP), 2012, pp. 20:120:6.
    8. 8)
      • 7. Vantigodi, S., Radhakrishnan, V.B.: ‘Action recognition from motion capture data using meta-cognitive RBF network classifier’. IEEE Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014, pp. 16.
    9. 9)
      • 4. Hu, M.C., Chen, C.W., Cheng, W.H., et al: ‘Real-time human movement retrieval and assessment with Kinect sensor’, IEEE Trans. Cybern., 2015, 45, (4), pp. 742753.
    10. 10)
      • 3. Choensawat, W., Choi, W., Hachimura, K.: ‘Similarity retrieval of motion capture data based on derivative features’, Adv. Comput. Intell. Intell. Inf., 2012, 16, (1), pp. 1323.
    11. 11)
      • 13. Ahmed, F., Paul, P.P., Gavrilova, M.L.: ‘DTW-based Kernel and rank-level fusion for 3D gait recognition using Kinect’, Visual Comput., 2015, 31, (6-8), pp. 915924.
    12. 12)
      • 5. Kapsouras, I., Nikolaidis, N.: ‘Action recognition in motion capture data using a bag of postures approach’. Int. Conf. Pattern Recognition (ICPR), 2014, pp. 26492654.
    13. 13)
      • 22. Müller, M., Baak, A., Seidel, H.: ‘Efficient and robust annotation of motion capture data’. ACM SIGGRAPH/Eurographics Symp. Computer Animation (SCA), 2009, pp. 1726.
    14. 14)
      • 19. Sedmidubsky, J., Valcik, J., Balazia, M., et al: ‘Gait recognition based on normalized walk cycles’. Int. Symp. Visual Computing (ISVC), 2012, pp. 1120.
    15. 15)
      • 1. Auvinet, E., Multon, F., Aubin, C.E., et al: ‘Detection of gait cycles in treadmill walking using a Kinect’, Gait Posture, 2015, 41, (2), pp. 722772.
    16. 16)
      • 23. Carnegie Mellon University: ‘Carnegie-Mellon Motion Capture (MoCap) Database’, http://mocap.cs.cmu.edu, 2003.
    17. 17)
      • 2. Valcik, J., Sedmidubsky, J., Balazia, M., et al: ‘Identifying walk cycles for human recognition’. Proc. Pacific Asia Workshop on Intelligence and Security Informatics (PAISI), 2012, pp. 127135.
    18. 18)
      • 24. Hall, M., Frank, E., Holmes, G., et al: ‘The WEKA data mining software: an update’, SIGKDD Explorations, 2009, 11, (1), pp. 1018.
    19. 19)
      • 21. Khoshelham, K.: ‘Accuracy analysis of Kinect depth data’. ISPRS Workshop Laser Scanning, 2011, vol. 38.
    20. 20)
      • 10. Ball, A., Rye, D., Ramos, F., et al: ‘Unsupervised clustering of people from ‘skeleton’ data’. Proc. ACM/IEEE Int. Conf. Human-Robot Interaction, 2012, pp. 225226.
    21. 21)
      • 14. Jiang, S., Wang, Y., Zhang, Y., et al: ‘Real time gait recognition system based on Kinect skeleton feature’. ACCV Workshops on Computer Vision, 2015 (LNCS, 9008), pp. 4657.
    22. 22)
      • 6. Leightley, D., Li, B., McPhee, J.S., et al: ‘Exemplar-based human action recognition with template matching from a stream of motion capture’. Image Analysis and Recognition, 2014 (LNCS, 8815), pp. 1220.
    23. 23)
      • 20. Sinha, A., Chakravarty, K., Bhowmick, B.: ‘Person identification using skeleton information from Kinect’. Advances in Computer-Human Interactions, 2013, pp. 101108.
    24. 24)
      • 17. Kwolek, B., Krzeszowski, T., Michalczuk, A., et al: ‘3D gait recognition using spatio-temporal motion descriptors’, Intell. Inf. Database Syst. (ACIIDS), 2014, 8398, pp. 595604.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2015.0072
Loading

Related content

content/journals/10.1049/iet-bmt.2015.0072
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address