A Semi-Supervised Method for Surveillance-Based Visual Location Recognition
提出一种结合无监督子空间对齐和半监督拉普拉斯支持向量机的方法，解决手机与监控摄像头图像之间的跨设备位置识别问题，并分析时间、天气等环境因素的影响。
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.