Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition

Human gesture recognition has drawn much attention in the area of computer vision. However, the performance of gesture recognition is always influenced by some gesture-irrelevant factors like the background and the clothes of performers. Therefore, focusing on the regions of hand/arm is important to the gesture recognition. Meanwhile, a more adaptive architecture-searched network structure can also perform better than the block-fixed ones like Resnet since it increases the diversity of features in different stages of the network better. In this paper, we propose a regional attention with architecture-rebuilt 3D network (RAAR3DNet) for gesture recognition. We replace the fixed Inception modules with the automatically rebuilt structure through the network via Neural Architecture Search (NAS), owing to the different shape and representation ability of features in the early, middle, and late stage of the network. It enables the network to capture different levels of feature representations at different layers more adaptively. Meanwhile, we also design a stackable regional attention module called dynamic-static Attention (DSA), which derives a Gaussian guidance heatmap and dynamic motion map to highlight the hand/arm regions and the motion information in the spatial and temporal domains, respectively. Extensive experiments on two recent large-scale RGB-D gesture datasets validate the effectiveness of the proposed method and show it outperforms state-of-the-art methods. The codes of our method are available at: https://github.com/zhoubenjia/RAAR3DNet.Comment: Accepted by AAAI 202

Gesture spotting and recognition using salience detection and concatenated hidden markov models

We developed a gesture salience based hand tracking method, and a gesture spotting and recognition method based on concatenated hidden Markov models. A 3-fold cross validation using the ChAirGest development data set with 10 users gives an F1 score of 0.907 and an accurate temporal segmentation rate (ATSR) of 0.923. The average final score is 0.9116. Compared with using the hand joint position from the Kinect SDK, using our hand tracking method gives a 3.7% absolute increase in the recognition F1 score