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—Recently, depth data is widely used in computer vision applications such as detection and tracking, which shows great promises in complicated environments due to its complementary natures to RGB data. However, previous works mostly use depth as an auxiliary cue of RGB data and overlook its inherent advantage on motion detection. Intrinsically different(More)
Person re-identification is valuable for intelligent video surveillance and has drawn wide attention. Although person re-identification research is making progress, it still faces some challenges such as varying poses, illumination and viewpoints. As a major aspect of person re-identification, feature representation has been widely researched. Low-level(More)
Person orientation estimation is valuable for intelligent video surveillance. Although much progress has been made in recent years, it still faces challenges such as varying poses, illuminations and viewpoints. Most existing approaches merely use appearance information or combine it with motion information. Appearance-based classifiers are trained offline(More)
—Person re-identification (re-id) consists of associating individual across camera network, which is valuable for intelligent video surveillance and has drawn wide attention. Although person re-identification research is making progress, it still faces some challenges such as varying poses, illumination and viewpoints. For feature representation in(More)
Recently, depth data is widely used in computer vision applications such as detection and tracking, which shows great promises in complicated environments due to its complementary natures to RGB data. However, previous works mostly use depth as an auxiliary cue of RGB data and overlook its inherent advantage on motion detection. Intrinsically different from(More)
This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG$^2$ utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the(More)
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