Action tube extraction based 3D-CNN for RGB-D action recognition

In this paper we propose a novel action tube extractor for RGB-D action recognition in trimmed videos. The action tube extractor takes as input a video and outputs an action tube. The method consists of two parts: spatial tube extraction and temporal sampling. The first part is built upon MobileNet-SSD and its role is to define the spatial region where the action takes place. The second part is based on the structural similarity index (SSIM) and is designed to remove frames without obvious motion from the primary action tube. The final extracted action tube has two benefits: 1) a higher ratio of ROI (subjects of action) to background; 2) most frames contain obvious motion change. We propose to use a two-stream (RGB and Depth) I3D architecture as our 3D-CNN model. Our approach outperforms the state-of-the-art methods on the OA and NTU RGB-D datasets. © 2018 IEEE.Peer ReviewedPostprint (published version

An Airfield Minimum Operating Strip Selection Method Based on the TOPSIS Method

With Multiple Attribute Decision Making (MADM) method, the selection of airfield Minimum Operating Strip (MOS) schemes is researched. The influencing factors of the selection decision making are analyzed. The weight values of these factors are determined. Then the decision making optimization method based on TOPSIS is presented. At last, the feasibility of the method is validated with a calculation example