{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:43:05Z","timestamp":1770291785405,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T00:00:00Z","timestamp":1587081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The human visual system can recognize a person based on his physical appearance, even if extreme spatio-temporal variations exist. However, the surveillance system deployed so far fails to re-identify the individual when it travels through the non-overlapping camera\u2019s field-of-view. Person re-identification (Re-ID) is the task of associating individuals across disjoint camera views. In this paper, we propose a robust feature extraction model named Discriminative Local Features of Overlapping Stripes (DLFOS) that can associate corresponding actual individuals in the disjoint visual surveillance system. The proposed DLFOS model accumulates the discriminative features from the local patch of each overlapping strip of the pedestrian appearance. The concatenation of histogram of oriented gradients, Gaussian of color, and the magnitude operator of CJLBP bring robustness in the final feature vector. The experimental results show that our proposed feature extraction model achieves rank@1 matching rate of 47.18% on VIPeR, 64.4% on CAVIAR4REID, and 62.68% on Market1501, outperforming the recently reported models from the literature and validating the advantage of the proposed model.<\/jats:p>","DOI":"10.3390\/sym12040647","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T05:48:52Z","timestamp":1587448132000},"page":"647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Person Re-Identification by Discriminative Local Features of Overlapping Stripes"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3860-2635","authenticated-orcid":false,"family":"Fawad","sequence":"first","affiliation":[{"name":"ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8616-3959","authenticated-orcid":false,"given":"Muhammad Jamil","family":"Khan","sequence":"additional","affiliation":[{"name":"ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5084-7862","authenticated-orcid":false,"given":"MuhibUr","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, C., Wang, J., Liu, L., Yu, J.G., and Sang, N. (2019). Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification. Sensors, 19.","DOI":"10.3390\/s19183861"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jamil Khan, M., Rahman, M., Amin, Y., and Tenhunen, H. (2019). Low-Rank Multi-Channel Features for Robust Visual Object Tracking. Symmetry, 11.","DOI":"10.3390\/sym11091155"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5338","DOI":"10.1109\/TIP.2018.2851098","article-title":"Cross-view discriminative feature learning for person re-identification","volume":"27","author":"Borgia","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wu, L., Hong, R., Wang, Y., and Wang, M. (2019). Cross-entropy adversarial view adaptation for person re-identification. IEEE Trans. Circuits Syst. Video Technol.","DOI":"10.1109\/TCSVT.2019.2909549"},{"key":"ref_5","unstructured":"Liu, F., and Zhang, L. (November, January 27). View Confusion Feature Learning for Person Re-identification. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107036","DOI":"10.1016\/j.patcog.2019.107036","article-title":"Deep-person: Learning discriminative deep features for person re-identification","volume":"98","author":"Bai","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TPAMI.2017.2666805","article-title":"Person re-identification by camera correlation aware feature augmentation","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","first-page":"523","article-title":"A systematic evaluation and benchmark for person re-identification: Features, metrics, and datasets","volume":"41","author":"Gou","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shen, Y., Xiao, T., Li, H., Yi, S., and Wang, X. (2018, January 18\u201323). End-to-end deep kronecker-product matching for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00720"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, X., Zhang, Z., and Huang, K. (2017, January 21\u201326). Learning deep contextaware features over body and latent parts for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.782"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., and Smith, J.R. (2013, January 23\u201328). Learning locally-adaptive decision functions for person verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.463"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Cao, D., and Li, S. (2017, January 21\u201326). Re-ranking person reidentification with k-reciprocal encoding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.389"},{"key":"ref_13","unstructured":"Zhang, L., Xiang, T., and Gong, S. (July, January 26). Learning a discriminative null space for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shen, Y., Li, H., Xiao, T., Yi, S., Chen, D., and Wang, X. (2018, January 18\u201323). Deep groupshuffling random walk for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00241"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"183692","DOI":"10.1109\/ACCESS.2019.2959326","article-title":"Image Local Features Description Through Polynomial Approximation","volume":"7","author":"Fawad","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110116","DOI":"10.1109\/ACCESS.2019.2932687","article-title":"Robustness-driven hybrid descriptor for noise-deterrent texture classification","volume":"7","author":"Saeed","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pedagadi, S., Orwell, J., Velastin, S.A., and Boghossian, B.A. (2013, January 23\u201328). Local fisher discriminant analysis for pedestrian re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.426"},{"key":"ref_19","unstructured":"Chen, D., Yuan, Z., Chen, B., and Zheng, N. (July, January 26). Similarity learning with spatial constraints for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zheng, W., Gong, S., and Xiang, T. (2011, January 20\u201325). Person re-identification by probabilistic relative distance comparison. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995598"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liao, S., Hu, Y., Zhu, X., and Li, S.Z. (2015, January 7\u201312). Person re-identification by local maximal occurrence representation and metric learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298832"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Asghar, M.A., Khan, M.J., Amin, Y., Rizwan, M., Rahman, M., Badnava, S., and Mirjavadi, S.S. (2019). EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach. Sensors, 19.","DOI":"10.3390\/s19235218"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ma, B., Su, Y., and Jurie, F. (2014). Discriminative image descriptors for person reidentification. Person Re-Identification, Springer.","DOI":"10.1007\/978-1-4471-6296-4_2"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ma, B., Su, Y., and Jurie, F. (2012, January 3\u20137). BiCov: A novel image representation for person re-identification and face verification. Proceedings of the British Machive Vision Conference, Guildford, UK.","DOI":"10.5244\/C.26.57"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Farenzena, M., Bazzani, L., Perina, A., Murino, V., and Cristani, M. (2010, January 13\u201318). Person re-identification by symmetry-driven accumulation of local features. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539926"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2860","DOI":"10.1109\/TIP.2019.2891888","article-title":"Deep representation learning with part loss for person re-identification","volume":"28","author":"Yao","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1109\/TPAMI.2014.2369055","article-title":"Person re-identification by iterative re-weighted sparse ranking","volume":"37","author":"Lisanti","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","unstructured":"Matsukawa, T., Okabe, T., Suzuki, E., and Sato, Y. (July, January 26). Hierarchical gaussian descriptor for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1109\/TIP.2018.2878505","article-title":"Video person re-identification by temporal residual learning","volume":"28","author":"Dai","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.imavis.2014.04.002","article-title":"Covariance descriptor based on bio-inspired features for person re-identification and face verification","volume":"32","author":"Ma","year":"2014","journal-title":"Image Vis. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Matsukawa, T., Okabe, T., and Sato, Y. (2014, January 24\u201328). Person re-identification via discriminative accumulation of local features. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.681"},{"key":"ref_32","unstructured":"Chen, T., Ding, S., Xie, J., Yuan, Y., Chen, W., Yang, Y., and Wang, Z. (November, January 27). Abd-net: Attentive but diverse person re-identification. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shen, Y., Li, H., Yi, S., Chen, D., and Wang, X. (2018, January 8\u201314). Person re-identification with deep similarity-guided graph neural network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_30"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.patcog.2017.08.029","article-title":"Deep adaptive feature embedding with local sample distributions for person re-identification","volume":"73","author":"Wu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xiong, F., Gou, M., Camps, O., and Sznaier, M. (2014). Person re-identification using kernel-based metric learning methods. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10584-0_1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TPAMI.2007.250598","article-title":"Graph embedding and extensions: A general framework for dimensionality reduction","volume":"29","author":"Yan","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., and Bischof, H. (2012, January 16\u201321). Large scale metric learning from equivalence constraints. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247939"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mignon, A., and Jurie, F. (2012, January 16\u201321). Pcca: A new approach for distance learning from sparse pairwise constraints. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247987"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Khan, F.S., Felsberg, M., and Weijer, J.V.D. (2014, January 23\u201328). Adaptive color attributes for real-time visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.143"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"66668","DOI":"10.1109\/ACCESS.2019.2918004","article-title":"Texture Representation through Overlapped Multi-oriented Tri-scale Local Binary Pattern","volume":"7","author":"Fawad","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Karanam, S., Li, Y., and Radke, R. (2015, January 7\u201312). Sparse re-id: Block sparsity for person re-identification. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2015, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301392"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gong, S., Liu, C., Ji, Y., Zhong, B., Li, Y., and Dong, H. (2019). Image Understanding-Person Re-identification. Advanced Image and Video Processing Using MATLAB, Springer.","DOI":"10.1007\/978-3-319-77223-3"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/4\/647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:45:01Z","timestamp":1760363101000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/4\/647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,17]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["sym12040647"],"URL":"https:\/\/doi.org\/10.3390\/sym12040647","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,17]]}}}