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Recently, several effective features were proposed for person re-identification, such as Weight Histograms of Overlapping Stripes (WHOS) and Local Maximal Occurrence (LOMO), but it still need to explore new effective feature to improve the precision for person re-identification. So, in this paper, we proposed a new Dual Channel Gradient feature, which can be fused with WHOS and LOMO by directly concatenating...
Many features have been proposed for person re-identification, but most of them are the combination of several different kinds of single features. And there is no research about what role the single features play and which can be fused together to improve the performance. In this paper, we explore eight single features (four colors, two textures, two gradients) and their fusions. Evaluations are conducted...
This paper presents an orientation estimate scheme using monocular camera and inertial measurement units (IMUs). Unlike the traditional wearable orientation estimation methods, our proposed approach combines both of these two modalities in a novel pattern. Firstly, two visual correspondences between consecutive frames are selected that not only meet the requirement of descriptor similarity constraint,...
Human re-identification across cameras with non-overlapping fields of view is one of the most important and difficult problems in video surveillance and analysis. However, current algorithms are likely to fail in real-world scenarios for several reasons. For example, surveillance cameras are typically mounted high above the ground plane, causing serious perspective changes. Also, most algorithms approach...
Human re-identification remains one of the fundamental, difficult problems in video surveillance and analysis. Current metric learning algorithms mainly focus on finding an optimized vector space such that observations of the same person in this space have a smaller distance than observations of two different people. In this paper, we propose a novel metric learning approach to the human reidentification...
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