首页|Findings in Robotics and Automation Reported from Zhejiang University (Deforming Garment Classification With Shallow Temporal Extraction and Tree-based Fusion)
Findings in Robotics and Automation Reported from Zhejiang University (Deforming Garment Classification With Shallow Temporal Extraction and Tree-based Fusion)
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IEEE
Investigators discuss new findings in Robotics Robotics and Automation. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “A novel RGB-based continuous perception garment classification approach is proposed in this letter, with the aim of identifying the correct category of the garment from a set of categories. It has been observed that treating a video of the continuous deformation of cloth as a set of disordered static figures leads to low classification precision.” Financial support for this research came from Natural Science Foundation of Zhejiang Province. Our news editors obtained a quote from the research from Zhejiang University, “On the contrary, investigating the temporal information between frames can significantly improve the quality of extracted features and increase classification performance. In this regard, we propose a hybrid temporal fusion RGBbased algorithm, including an improved image-level shallow temporal feature extraction module (STEM) and a binary-tree fusion module (BiTF) for adaptive feature fusion. STEM incorporates multi-scale optical flow and long-short-term memorised information to capture both static features in every single image and dynamic features in consecutive images. BiTF constructs a tree-shaped structure to fuse an arbitrary number of extracted features in a video.”
HangzhouPeople’s Republic of ChinaAsiaRobotics and AutomationRoboticsAlgorithmsZhejiang University
NETL
NSTL
IEEE
