Improving Person Re-Identification Rate in Security Cameras by Orthogonal Moments and a Distance-based Criterion

  • Ali Dadkhah Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
  • Saeed Nasri Department of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Keywords: Person re-identification, Orthogonal Moments, Mahalanobis Distance

Abstract

Surveillance and security cameras help security forces in public places such as airports, railway stations, universities and office buildings to perform high-level surveillance tasks such as detecting suspicious activity or anticipating undesirable events. Re-Identification (Re-ID) is defined as the process of communicating between images of the person in different cameras in a surveillance environment. Changing the field of view of any camera presents challenges such as changing body posture, changing brightness, noise and blockage. This article focuses on extracting the most distinctive features to overcome these challenges. The features of Hu moment, Zernike moment in 9th order and Legendre moment in 9th order for each image are extracted and merged into a single feature vector to form a single feature vector for each image. Principal Component Analysis (PCA) was used to reduce the vector dimensionality and finally the Mahalanobis distance criterion was used for identification. The proposed method in the VIPeR database has achieved a re-ID rate of 96.5. Although the presented method is simple, the outcome has been superior compared to many of the state-of-the-art methods.

References

[1] D. Yi. Z. Lei. S. Liao. and S. Z. Li. "Deep metric learning for person re-identification." in 2014 22nd International Conference on Pattern Recognition. IEEE. pp. 34-39., 2014.
[2] C. Sun. D. Wang. and H. Lu. "Person re-identification via distance metric learning with latent variables." IEEE Transactions on Image Processing. Vol. 26, No. 1, pp. 23-34, 2017.
[3] A. Bedagkar-Gala and S. K. Shah. "A survey of approaches and trends in person re-identification." Image and Vision Computing. Vol. 32, No. 4, pp. 270-286. 2014.
[4] R. Vezzani. D. Baltieri. and R. Cucchiara. "People reidentification in surveillance and forensics: A survey." ACM Computing Surveys (CSUR). Vol. 46, No. 2, pp. 29, 2013.
[5] L. Bazzani. M. Cristani. A. Perina. and V. Murino. "Multiple-shot person re-identification by chromatic and epitomic analyses." Pattern Recognition Letters. Vol. 33, No. 7, pp. 898-903, 2012.
[6] N. Jojic. A. Perina. M. Cristani. V. Murino. and B. Frey. "Stel component analysis: Modeling spatial correlations in image class structure." in 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE. pp. 2044-205, 2009.
[7] L. Bazzani. M. Cristani. and V. Murino. "Symmetry-driven accumulation of local features for human characterization and re-identification." Computer Vision and Image Understanding. Vol. 117, No. 2, pp. 130-144, 2013.
[8] J. Metzler. "Appearance-based re-identification of humans in low-resolution videos using means of covariance descriptors." in 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance. IEEE. pp. 191-196, 2012.
[9] B. Ma. Y. Su. and F. Jurie. "Bicov: a novel image representation for person re-identification and face verification." in British Machive Vision Conference. pp. 11, 2012.
[10] Y. Zhang and S. Li. "Gabor-LBP based region covariance descriptor for person re-identification." in 2011 Sixth International Conference on Image and Graphics. IEEE. pp. 368-371, 2011.
[11] A. D'Angelo and J.-L. Dugelay. "People re-identification in camera networks based on probabilistic color histograms." in Visual Information Processing and Communication II. vol. 7882: International Society for Optics and Photonics. pp. 78820K, 2011.
[12] Z. J. Xiang. Q. Chen. and Y. Liu. "Person re-identification by fuzzy space color histogram." Multimedia tools and applications. Vol. 73, No. 1, pp. 91-107, 2014.
[13] R. Zhao. W. Ouyang. and X. Wang. "Person re-identification by salience matching." in Proceedings of the IEEE International Conference on Computer Vision. pp. 2528-2535, 2013.
[14] C. C. Loy. C. Liu. and S. Gong. "Person re-identification by manifold ranking." in 2013 IEEE International Conference on Image Processing. IEEE. pp. 3567-3571, 2013.
[15] R. Zhao. W. Ouyang. and X. Wang. "Unsupervised salience learning for person re-identification." in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3586-3593, 2013.
[16] J. Flusser. B. Zitova. and T. Suk. "Moments and moment invariants in pattern recognition." John Wiley & Sons. 2009.
[17] S. Paisitkriangkrai, Ch. Shen, and Anton van den Hengel. "Learning to rank in person re-identi_cation " with metric ensembles." CoRR, abs/1503.01543, 2015.
Published
2020-12-01
How to Cite
Dadkhah, A., & Nasri, S. (2020). Improving Person Re-Identification Rate in Security Cameras by Orthogonal Moments and a Distance-based Criterion. Majlesi Journal of Electrical Engineering, 14(4), 85-91. https://doi.org/https://doi.org/10.29252/mjee.14.4.85
Section
Articles