论文标题
时空多层的时空多层感知器，以识别手势
A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
论文作者
论文摘要
手势识别对于自动驾驶汽车与人类的相互作用至关重要。尽管当前的方法着重于结合几种模式，例如图像特征，关键点和骨向量，但我们提出了神经网络体系结构，该结构仅通过身体骨架输入数据提供最新的结果。我们建议在自动驾驶汽车的情况下提出时空多层的手势识别。给定的3D身体随着时间的推移而定，我们定义时间和空间混合操作以提取两个域中的特征。此外，每个时间步骤的重要性都会通过挤压和激发层重新加权。提供了对TCG和Drive＆ACT数据集的广泛评估，以展示我们方法的有希望的性能。此外，我们将模型部署到自动驾驶汽车上，以显示其实时功能和稳定的执行。
Gesture recognition is essential for the interaction of autonomous vehicles with humans. While the current approaches focus on combining several modalities like image features, keypoints and bone vectors, we present neural network architecture that delivers state-of-the-art results only with body skeleton input data. We propose the spatio-temporal multilayer perceptron for gesture recognition in the context of autonomous vehicles. Given 3D body poses over time, we define temporal and spatial mixing operations to extract features in both domains. Additionally, the importance of each time step is re-weighted with Squeeze-and-Excitation layers. An extensive evaluation of the TCG and Drive&Act datasets is provided to showcase the promising performance of our approach. Furthermore, we deploy our model to our autonomous vehicle to show its real-time capability and stable execution.
