Pose and Illumination Invariance of Attribute Detectors in Person Re-identification

  • Authors

    • Mohammadali Saghafi
    • Aini Hussain
    • Mohamad Hanif Md. Saad
    • Mohd Asyraf Zulkifley
    • Nooritawati Md Tahir
    • Mohd Faisal Ibrahim
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.11.20796
  • person re-identification, Attribute, metric learning.

  • The use of attributes in person re-identification and video surveillance applications has grabbed attentions of many researchers in recent times. Attributes are suitable tools for mid-level representation of a part or a region in an image as it is more similar to human perception as compared to the quantitative nature of the normal visual features description of those parts. Hence, in this paper, the preliminary experimental results to evaluate the robustness of attribute detectors against pose and light variations in contrast to the use of local appearance features is discussed. Results attained proven that the attribute-based detectors are capable to overcome the negative impact of pose and light variation towards person re-identification activities. In addition, the degree of importance of different attributes in re-identification is evaluated and compared with other previous works in this field.

     

     

  • References

    1. [1] Wang T, Gong S, Zhu X and Wang S, “Person re-identification by discriminative selection in video ranking,†IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (2016), 2501-2514.

      [2] Satta R, Fumera G and Roli F, “Exploiting dissimilarity representations for re-identification,†Proceedings of the International Workshop on Similarity-Based Pattern Recognition, (2011), pp. 275-289.

      [3] Farenzena M, Bazzani L, Perina A, Murino V and Cristani M, “Person re-identification by symmetry-driven accumulation of local features,†Proceedings of the International Conference on Computer Vision and Pattern Recognition, (2010), pp. 2360-2367.

      [4] Zheng W, Gong Sh and Xiang T, “Associating groups of people,†Proceedings of the British Machine Vision Conference, (2009), pp. 1.

      [5] Poongothai E, Suruliandi, A., “Survey on color, texture and shape features for person re-identification,†Indian Journal of Science and Technology, 9 (2016), pp. 1-7.

      [6] Poongothai, E, Suruliandi A, “color, texture and shape feature analysis for person re-identification technique,†Advances in Vision Computing: An International Journal, 3 (2016), 17-26

      [7] Farhadi A, Endres I, Hoiem D and Forsyth D, “Describing objects by their attributes,†Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2009), pp. 1778-1785.

      [8] Satta R, Pala F, Fumera G and Roli F, “People search with textual queries about clothing appearance attributes,†in S. Gong, M. Cristani, S. Yan, & C. Loy (Eds.), Person Re-Identification. London: Springer, (2014), pp. 371-389.

      [9] Layne R, Hospedales TM and Gong S, "Attributes-based re-identification,†in S. Gong, M. Cristani, S. Yan, & C. Loy (Eds.), Person Re-Identification. London: Springer, (2014), pp. 93-117.

      [10] Siddiquie B, Feris RS and Davis LS, “Image ranking and retrieval based on multi-attribute queries,†Proceedings of the Computer Vision and Pattern Recognition, (2011), pp. 801-808.

      [11] Li A, Liu L, Wang K, Liu S and Yan S, “Clothing attributes assisted person re-identification,†IEEE Transactions on Circuits and Systems for Video Technology, 25 (2015), 869-878.

      [12] Gray D, Brennan S, Tao H, “Evaluating appearance models for recognition, reacquisition and tracking,†Proceedings of the 10th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, (2007), pp. 1-7.

      [13] Jungseock J, Wang S and Zhu SC. "Human attribute recognition by rich appearance dictionary," Proceedings of the IEEE International Conference on Computer Vision, (2013), pp. 721-728,

      [14] Ferrari V, Zisserman A. “Learning visual attributes,†Proceedings of the Advances in Neural Information Processing Systems, (2008), pp. 433-440.

      [15] Ess A, Leibe B, Gool LV, “Depth and appearance for mobile scene analysis,†Proceedings of the IEEE 11th Int. Conf. on Computer Vision, (2007), pp. 1–8.

      [16] Layne R, Hospedales TM and Gong Sh, "Re-id: Hunting attributes in the wild," Proceedings of the BMVC, (2014), pp. 1709—1724

      [17] Prosser B, Zheng W, Gong Sh, Xiang T and Mary Q, "Person re-identification by support vector ranking," Proceedings of the British Machine Vision Conference, (2010), pp. 1-11.

      [18] Varior RR, Wang G, Lu J and Liu T, “Learning invariant color features for person re-identification,†IEEE Transactions on Image Processing, 25 (2016), 3395-3410.

      [19] McFee B, Lanckriet G, “Learning multi-modal similarity,†Journal of Machine Learning Research, 12 (2011), 491-523.

      [20] Barnich O, Van Droogenbroeck M, “ViBe: A universal background subtraction algorithm for video sequences,†IEEE Transactions on Image Processing, 20 (2011), 1709-1724.

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    Saghafi, M., Hussain, A., Hanif Md. Saad, M., Asyraf Zulkifley, M., Md Tahir, N., & Faisal Ibrahim, M. (2018). Pose and Illumination Invariance of Attribute Detectors in Person Re-identification. International Journal of Engineering & Technology, 7(4.11), 174-178. https://doi.org/10.14419/ijet.v7i4.11.20796