We present a joint camera and radar approach to enable autonomous vehicles to
understand and react to human gestures in everyday traffic. Initially, we
process the radar data with a PointNet followed by a spatio-temporal multilayer
perceptron (stMLP). Independently, the human body pose is extracted from the
camera frame and processed with a separate stMLP network. We propose a fusion
neural network for both modalities, including an auxiliary loss for each
modality. In our experiments with a collected dataset, we show the advantages
of gesture recognition with two modalities. Motivated by adverse weather
conditions, we also demonstrate promising performance when one of the sensors
lacks functionality.

本研究提出了一种联合摄像机和雷达方法，使自动驾驶车辆能够了解和响应日常交通中的人类手势，通过处理雷达和摄像机数据，利用融合神经网络提高手势识别的准确性。

基于关键点和雷达流融合的自动驾驶手势识别

Gesture Recognition with Keypoint and Radar Stream Fusion for Automated  Vehicles

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:
this https URL

本文使用神经结构搜索技术，并结合自适应的网络结构及局部注意力，提出了一种针对手势识别的 RAAR3DNet 方法，该方法有效地提取了手臂区域以及运动信息，并在两个大型 RGB-D 手势数据集上超越了现有技术。