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A Semi-Supervised Method for Surveillance-Based Visual Location Recognition
Nov 08, 2017Author:
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Title: A Semi-Supervised Method for Surveillance-Based Visual Location Recognition

 Authors: Liu, PC; Yang, PP; Wang, C; Huang, KQ; Tan, TN

 Author Full Names: Liu, Pengcheng; Yang, Peipei; Wang, Chong; Huang, Kaiqi; Tan, Tieniu

 Source: IEEE TRANSACTIONS ON CYBERNETICS, 47 (11):3719-3732; 10.1109/TCYB.2016.2578639 NOV 2017

 Language: English

 Abstract: In this paper, we are devoted to solving the problem of crossing surveillance and mobile phone visual location recognition, especially for the case that the query and reference images are captured by mobile phone and surveillance camera, respectively. Besides, we also study the influence of the environmental condition variations on this problem. To explore that problem, we first build a cross-device location recognition dataset, which includes images of 22 locations taken by mobile phones and surveillance cameras under different time and weather conditions. Then based on careful analysis of the problems existing in the data, we specifically design a method which unifies an unsupervised subspace alignment method and the semi-supervised Laplacian support vector machine. Experiments are performed on our dataset. Compared with several related methods, our method shows to be more efficient on the problem of crossing surveillance and mobile phone visual location recognition. Furthermore, the influence of several factors such as feature, time, and weather is studied.

 ISSN: 2168-2267

 eISSN: 2168-2275

 IDS Number: FJ8GS

 Unique ID: WOS:000413003100020

 PubMed ID: 27352403

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