{"type":"ScholarlyArticle","title":"Fast Vehicle Identification in Surveillance via Ranked Semantic Sampling Based Embedding","authors":[{"name":"Feng Zheng","slug":"feng-zheng","given":"Feng","family":"Zheng","affiliation":"Department of Electrical and Computer Engineering , University of Pittsburgh"},{"name":"Xin Miao","slug":"xin-miao","given":"Xin","family":"Miao","affiliation":"Department of Computer Sciences & Engineering , University of Texas at Arlington"},{"name":"Heng Huang","slug":"heng-huang","given":"Heng","family":"Huang","affiliation":"Department of Electrical and Computer Engineering , University of Pittsburgh"}],"abstract":"Identifying vehicles across cameras in traffic surveillance is fundamentally important for public safety purposes. However, despite some preliminary work, the rapid vehicle search in large-scale datasets has not been investigated. Moreover, modelling a view-invariant similarity between vehicle images from different views is still highly challenging. To address the problems, in this paper, we propose a Ranked Semantic Sampling (RSS) guided binary embedding method for fast cross-view vehicle Re-IDentification (Re-ID). The search can be conducted by efficiently computing similarities in the projected space. Unlike previous methods using random sampling, we design tree-structured attributes to guide the mini-batch sampling. The ranked pairs of hard samples in the mini-batch can improve the convergence of optimization. By minimizing a novel ranked semantic distance loss defined according to the structure, the learned Hamming distance is view-invariant, which enables cross-view Re-ID. The experimental results demonstrate that RSS outperforms the state-of-the-art approaches and the learned embedding from one dataset can be transferred to achieve the task of vehicle Re-ID on another dataset.","doi":"10.24963/ijcai.2018/514","url":"https://ijcai.org/proceedings/2018/514","pdf":"https://ijcai.org/proceedings/2018/0514.pdf","markdown":"https://ijcai.org/proceedings/2018/514.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/514","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":514,"pages":{"first":3697,"last":3703},"keywords":["Computer Vision: Computer Vision","Machine Learning Applications: Big data ; Scalability"],"videos":[],"references":[{"position":1,"raw":"Cross-view M[1] M","title":"Cross-view M","authors":null,"year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"M[M[6] M[7] V1-2 90.7 81.4 76.6 80.3 67.3 53.4 3.6 V3-4 95.7 92.9 89.6 91.9 87.8 78.9 9.0 V1-3 86.6 84.5 76.8 82.1 58.2 53.5 4.9 V3-5 97.7 93.0 92.3 92.5 88.4 86.4 6.2 V1-4 98.8 97.5 96.4 96.4 94.6 93.2 9.8 V4-1 95.8 96.0 93.0 93.9 90.3 89.8 11.4 V1-5 88.6 88.5 73.2 78.7 61.5 52.5 6.6 V4-2 89.3 88.8 76.1 84.5 66.4 59.5 4.9 V2-1 86.3 84.2 76.5 82.9 65.6 55.7 4.7 V4-3 92.0 90.0 84.0 86.2 76.5 76.0 9.5 V2-3 86.0 80.3 77.7 80.2 67.3 62.2 3.7 V4-5 94.7 91.8 85.7 85.5 79.7 75.8 7.6 V2-4 88.8 89.7 83.0 82.4 70.9 61.4 1.2 V5-1 88.7 85.5 71.7 79.3 63.2 56.2 4.2 V2-5 96.6 95.8 94.7 94.9 93.9 92.3 2.5 V5-2 95.0 95.6 91.8 92.8 88.8 88.7 4.4 V3-1 86.4 83.4 71.2 81.5 51.4 57.1 4.4 V5-3 93.2 90.8 86.9 88.8 79.7 82.3 6.0 V3-2 91.1 86.9 74.8 83.1 61.7 59.0 2.7 V5-4 95.1 92.7 79.9 88.0 73.6 78.0 5.6","title":null,"authors":"M, M","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Amid and Ukkonen, 2015] Ehsan Amid and Antti Ukko- nen. 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