Hand gesture-based human-computer interaction is an important problem that is
well explored using color camera data. In this work we proposed a hand gesture
detection system using thermal images. Our system is capable of handling
multiple hand regions in a frame and process it fast for real-time
applications. Our system performs a series of steps including background
subtraction-based hand mask generation, k-means based hand region
identification, hand segmentation to remove the forearm region, and a
Convolutional Neural Network (CNN) based gesture classification. Our work
introduces two novel algorithms, bubble growth and bubble search, for faster
hand segmentation. We collected a new thermal image data set with 10 gestures
and reported an end-to-end hand gesture recognition accuracy of 97%.

本文展示了一种使用热像数据的手势检测系统，使用基于背景减法的手掩模生成、k-means 算法手区域识别、手臂区域去除的手势分割以及基于卷积神经网络的手势分类来实现多个手区域的快速处理， 并引入了泡沫生长和泡沫搜索两种新算法， 进行更快的手势分割。我们收集了一个包含 10 种手势的新热像数据集，并报道了 97％的端到端手势识别准确率。

热像实时手势识别

Real-Time Hand Gesture Identification in Thermal Images

In recent years, the Thermal Image Super-Resolution (TISR) problem has become
an attractive research topic. TISR would been used in a wide range of fields,
including military, medical, agricultural and animal ecology. Due to the
success of PBVS-2020 and PBVS-2021 workshop challenge, the result of TISR keeps
improving and attracts more researchers to sign up for PBVS-2022 challenge. In
this paper, we will introduce the technical details of our submission to
PBVS-2022 challenge designing a Bilateral Network with Channel Splitting
Network and Transformer(BN-CSNT) to tackle the TISR problem. Firstly, we
designed a context branch based on channel splitting network with transformer
to obtain sufficient context information. Secondly, we designed a spatial
branch with shallow transformer to extract low level features which can
preserve the spatial information. Finally, for the context branch in order to
fuse the features from channel splitting network and transformer, we proposed
an attention refinement module, and then features from context branch and
spatial branch are fused by proposed feature fusion module. The proposed method
can achieve PSNR=33.64, SSIM=0.9263 for x4 and PSNR=21.08, SSIM=0.7803 for x2
in the PBVS-2022 challenge test dataset.

本文介绍了一个名为 BN-CSNT 的双边网络设计，通过频道分裂网络和 Transformer 解决了 TISR 问题，最终在 PBVS-2022 挑战测试数据集上实现了良好的表现，x4 的 PSNR 为 33.64，SSIM 为 0.9263，x2 的 PSNR 为 21.08，SSIM 为 0.7803。