Person Reidentification on Intelligent Surveillance System Using Bag of Visual Feature Methods
Abstract
Camera-based surveillance systems have been widely used to monitor public places for ensuring security and safety. One of the problems in the surveillance process is identifying human objects on different CCTV cameras, which is referred to as person reidentification (Re-ID). Re-ID is the process of identifying whether the images of human objects, captured from two or more images from CCTV cameras with the different viewpoints, are the same person or not. This paper proposes a method based on visual features of the object image, named as Bag of Visual Feature (BOVF). BOVF works by representing image data as a collection of local features that are used with a feature clustering mechanism. BOVF implementation uses the Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering methods in the Histogram of Oriented Gradient (HOG) features. The results of this study with 70 image frames from iLIDS-VID dataset obtained the best accuracy at R-20 by 88% using DBSCAN with a processing speed of 1.85 seconds.
References
A. Bedagkar-Gala dan S.K. Shah, “A Survey of Approaches and Trends in Person Re-Identification,” Image and Vision Computing, Vol. 32, No. 4, hal. 270–286, 2014.
Wahyono, A. Filonenko, dan K.H. Jo, “Unattended Object Identification for Intelligent Surveillance Systems Using Sequence of Dual Background Difference,” IEEE Transactions on Industrial Informatics, Vol. 12, No. 6, hal. 2247–2255, 2016.
M. Farenzena, L. Bazzani, A. Perina, V. Murino, dan M. Cristani, “Person Re-Identification by Symmetry-Driven Accumulation of Local Features,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, hal. 2360–2367.
R. Zhao, W. Ouyang, dan X. Wang, “Unsupervised Salience Learning for Person Re-Identification,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, hal. 3586–3593.
M.Y. Manshur, “Implementasi Bag of Visual Feature Menggunakan Algoritme Pengelompokan DBSCAN untuk Person Re-Identification,” B. Comp. thesis, Universitas Gadjah Mada, Yogyakarta, Indonesia, Jan. 2020.
D. Yi, Z. Lei, dan S. Z. Li, “Deep Metric Learning for Practical Person Re-Identification,” CoRR, abs/1407.4, 2014.
D. Wu, S.-J. Zheng, X.-P. Zhang, C.-A. Yuan, F. Cheng, Y. Zhao, Y.-J. Lin, Z.-Q. Zhao, Y.-L. Jiang, dan D.-S. Huang, “Deep Learning-based Methods for Person Re-Identification: A Comprehensive Review,” Neurocomputing, Vol. 337, hal. 354-371, 2019.
L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, dan Q. Tian, “Scalable Person Re-identification: A Benchmark,” IEEE International Conference on Computer Vision (ICCV), 2015, hal. 1116–1124.
Wahyono dan K.H. Jo, “Person Re-Identification Using Bag of Visual Feature on Random Windows,” dipresentasikan pada International Workshop of Frontiers in Computer Vision, Seoul, Korea, 2017.
N. Dalal dan B. Triggs, “Histograms of Oriented Gradients for Human Detection,” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR05), 2005, hal. 1-8.
J. Sander, M. Ester, H.-P. Kriegel, dan Xiaowei Xu, “Density-based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications,” Data Mining and Knowledge Discovery, Vol. 2, No. 2, hal. 169–194, 1998.
K. Khan, S.U. Rehman, K. Aziz, S. Fong, dan S. Sarasvady, “DBSCAN: Past, Present, and Future,” 5th International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), 2014, hal. 232–238.
D. Birant dan A. Kut, “ST-DBSCAN: An Algorithm for Clustering Spatial–temporal Data,” Data & Knowledge Engineering, Vol. 60, No. 1, hal. 208–221, 2007.
R.M. Bolle, “The Relation Between the ROC Curve and the CMC,” Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID’05), 2005, hal. 15–20.
M. Li, X. Zhu dan S. Gong, "Unsupervised Person Re-Identification by Deep Learning Tracklet Association," Proc. European Conference on Computer Vision, 2018, hal. 1-17.
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