A Semi-Supervised Method for Surveillance-Based Visual Location Recognition

作者：Pengcheng Liu, Peipei Yang, Chong Wang, Kaiqi Huang, Tieniu Tan · 发表于：IEEE Transactions on Cybernetics · 年份：2016 · DOI：10.1109/tcyb.2016.2578639 · 被引用次数：22 · 研究领域：Advanced Image and Video Retrieval Techniques、Video Surveillance and Tracking Methods、Remote-Sensing Image Classification

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.