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Person re-identification based on pose angle estimation and multi-feature extraction

Person re-identification based on pose angle estimation and multi-feature extraction

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Re-identification enables the tracking of the person taken from different disjoint camera aspects either from online or retrospectively for recognition of his or her visual appearance. Here a new method is proposed for person re-identification, taking into consideration the pose of the person as the primary factor, with multiple features being extracted from significant portions. Then angle-based pose priority has applied for matching and identification more robust to viewpoint. Their proposed method helps to reduce the number of images which are redundant in the training phase as well as the number of matching process in the test phase. The strength of the proposed method is demonstrated on three different benchmark databases containing more than 1000 person-images under variations in illumination, viewpoint and occlusion. The experimental results show that the proposed approach provides a higher recognition rates for all the issues of identification process. Finally, the results prove the superiority of the proposed method over other re-identification methods both in terms of visual and quantitative comparisons.

References

    1. 1)
      • M. Guillaumin , J. Verbeek , C. Schmid .
        1. Guillaumin, M., Verbeek, J., Schmid, C.: ‘Is that you? Metric learning approaches for face identification’. 12th Int. Conf. Computer Vision, Kyoto, Japan, 2009, pp. 498505.
        . 12th Int. Conf. Computer Vision , 498 - 505
    2. 2)
      • L. Mirmohamadsadeghi , A. Drygajlo .
        2. Mirmohamadsadeghi, L., Drygajlo, A.: ‘Palm vein recognition with local binary patterns and local derivative patterns’. Int. Conf. Biometrics, Washington, USA, 2011.
        . Int. Conf. Biometrics
    3. 3)
      • F.M. Castro , M.J. Marín-Jiménez , N. Guil .
        3. Castro, F.M., Marín-Jiménez, M.J., Guil, N.: ‘Multimodal features fusion for gait, gender and shoes recognition’, Mach. Vis. Appl.', 2016, 27, (8), pp. 12131228.
        . Mach. Vis. Appl.' , 8 , 1213 - 1228
    4. 4)
      • M.-H. Cheng , M.-F. Ho , C.-L. Huang .
        4. Cheng, M.-H., Ho, M.-F., Huang, C.-L.: ‘Gait analysis for human identification through manifold learning and HMM’, Pattern Recognit., 2008, 41, (8), pp. 25412553.
        . Pattern Recognit. , 8 , 2541 - 2553
    5. 5)
      • E. Poongothai , A. Suruliandi .
        5. Poongothai, E., Suruliandi, A.: ‘Features analysis of person re-identification techniques’. Int. Conf. Computing Technologies and Intelligent Data Engineering, India, 2016.
        . Int. Conf. Computing Technologies and Intelligent Data Engineering
    6. 6)
      • M. Eisenbach , A. Kolarow , K. Schenk .
        6. Eisenbach, M., Kolarow, A., Schenk, K., et al: ‘View invariant appearance-based person re-identification using fast online feature selection and score level fusion’. Int. Conf. Advanced Video and Signal-Based Surveillance, Beijing, China, 2012, pp. 184190.
        . Int. Conf. Advanced Video and Signal-Based Surveillance , 184 - 190
    7. 7)
      • N. Martinel , C. Micheloni , GL. Foresti .
        7. Martinel, N., Micheloni, C., Foresti, GL.: ‘A pool of multiple person re-identification experts’, Pattern Recognit. Lett., 2016, 71, pp. 2330.
        . Pattern Recognit. Lett. , 23 - 30
    8. 8)
      • C.P. De , R. Felipe , W.R. Schwartz .
        8. De, C.P., Felipe, R., Schwartz, W.R.: ‘CBRA: color-based ranking aggregation for person re-identification’. Int. Conf. Image Processing, QC, Canada, 2015.
        . Int. Conf. Image Processing
    9. 9)
      • M. Tapaswi , B. Martin .
        9. Tapaswi, M., Martin, B., et al: ‘‘Knock! Knock! Who is it?’ Probabilistic person identification in TV-series’. 2012 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), RI, USA, 2012.
        . 2012 IEEE Conf. Computer Vision and Pattern Recognition (CVPR)
    10. 10)
      • I. Kviatkovsky , A. Adam , E. Rivlin .
        10. Kviatkovsky, I., Adam, A., Rivlin, E.: ‘Colour invariants for person re-identification’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (7), pp. 16221634.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 7 , 1622 - 1634
    11. 11)
      • B. Ma , Y. Su , F. Jurie .
        11. Ma, B., Su, Y., Jurie, F.: ‘Bicov: a novel image representation for person re-identification and face verification’. Int. Conf. Machine Vision, Guildford, UK, 2012.
        . Int. Conf. Machine Vision
    12. 12)
      • N. Martinel , C. Micheloni .
        12. Martinel, N., Micheloni, C.: ‘Person re-identification by modelling principal component analysis coefficients of image dissimilarities’, Electron. Lett., 2014, 50, (14), pp. 10001001.
        . Electron. Lett. , 14 , 1000 - 1001
    13. 13)
      • S. Pedagadi , J. Orwell , S. Velastin .
        13. Pedagadi, S., Orwell, J., Velastin, S., et al: ‘Local Fisher discriminant analysis for pedestrian re-identification’. Int. Conf. Computer Vision and Pattern Recognition, OR, USA, 2013, pp. 33183325.
        . Int. Conf. Computer Vision and Pattern Recognition , 3318 - 3325
    14. 14)
      • C. Liu , S. Gong , C.C. Loy . (2014)
        14. Liu, C., Gong, S., Loy, C.C., et al: ‘Evaluating feature importance for re-identification’, in Gong, S., Cristani, M., Yan, S., Loy, C.C. (Eds.) ‘Person re-identification’ (Springer Press, London, 2014), pp. 203228.
        .
    15. 15)
      • D. Slater , G. Healey .
        15. Slater, D., Healey, G.: ‘The illumination-invariant recognition of 3D objects using local color invariants’, IEEE Trans. Pattern Anal. Mach. Intell., 1996, 18, pp. 206210.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 206 - 210
    16. 16)
      • D.S. Kim , M. Kim , B.S. Kim .
        16. Kim, D.S., Kim, M., Kim, B.S., et al: ‘Histograms of local intensity differences for pedestrian classification in far-infrared images’, Electron. Lett., 2013, 49, (4), pp. 258260.
        . Electron. Lett. , 4 , 258 - 260
    17. 17)
      • M. Ye , C. Liang , Y. Yu .
        17. Ye, M., Liang, C., Yu, Y., et al: ‘Person re-identification via ranking aggregation of similarity pulling and dissimilarity pushing’, IEEE Trans. Multimed., 2016, 18, (12), p. 2553.
        . IEEE Trans. Multimed. , 12 , 2553
    18. 18)
      • W. Cao , H. Han , X.K. Sun .
        18. Cao, W., Han, H., Sun, X.K., et al: ‘Target re-identification based on adaptive incremental KISS measure learning’. Memetic Computing, 2016, pp. 18.
        . Memetic Computing , 1 - 8
    19. 19)
      • A. Mignon , F. Jurie .
        19. Mignon, A., Jurie, F.: ‘PCCA: a new approach for learning distances from sparse pairwise constraints’. Asian Conf. Computer Vision, South Korea, 2012.
        . Asian Conf. Computer Vision
    20. 20)
      • D.S. Cheng , M. Cristani , M. Stoppa .
        20. Cheng, D.S., Cristani, M., Stoppa, M., et al: ‘Custom pictorial structures for re-identification’, Int. British Machine Conf. Machine Vision, 2011, pp. 111.
        . British Machine Conf. Machine Vision , 1 - 11
    21. 21)
      • R. Satta , G. Fumera , F. Roli .
        21. Satta, R., Fumera, G., Roli, F., et al: ‘A multiple component matching framework for person re-identification’. Int. Conf. Image Analysis and Processing, Berlin, Heidelberg, 2011, pp. 140149.
        . Int. Conf. Image Analysis and Processing , 140 - 149
    22. 22)
      • Z. Wu , Y. Li , R.J. Radke .
        22. Wu, Z., Li, Y., Radke, R.J.: ‘Viewpoint invariant human re-identification in camera networks using pose priors and subject-discriminative features’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (5), pp. 10951108.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 5 , 1095 - 1108
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