Method for reconstructing safety and arming motion process by integrating Kalman filter and KCF.

This paper addresses the challenge of reconstructing the motion process of the safety and arming (S&A) mechanism in fuze by transforming the problem into a target detection and tracking problem. A novel tracking method, which fuses an improved Kalman filter with a temporal scale-adaptive KCF (AKF-CF), is proposed. The methodology introduces key innovations: (1) Extraction of grayscale images and directional gradient histogram (HOG) features of the target, followed by the use of an Adaptive Wave PCA-Autoencoder (AWPA) method to accurately capture multi-modal and multi-scale features of the target; (2) Application of bilinear interpolation and hybrid filtering techniques to generate a spatial and temporal scale-adaptive bounding box for the filtered target, enabling dynamic adjustment of the tracking box size; (3) Integration of an occlusion-aware mechanism using average peak correlation energy (APCE) to trigger Kalman-based position prediction when the target is occluded, thus mitigating tracking drift.

Skeleton-based Chinese sign language recognition and generation for bidirectional communication between deaf and hearing people.

Chinese sign language (CSL) is one of the most widely used sign language systems in the world. As such, the automatic recognition and generation of CSL is a key technology enabling bidirectional communication between deaf and hearing people. Most previous studies have focused solely on sign language recognition (SLR), which only addresses communication in a single direction.

Human Motion Retrieval Based on Statistical Learning and Bayesian Fusion.

A novel motion retrieval approach based on statistical learning and Bayesian fusion is presented. The approach includes two primary stages. (1) In the learning stage, fuzzy clustering is utilized firstly to get the representative frames of motions, and the gesture features of the motions are extracted to build a motion feature database.