论文标题
低分辨率热成像仪的低延迟手势识别
Low-latency hand gesture recognition with a low resolution thermal imager
论文作者
论文摘要
如今，使用手势接听电话或在驾驶汽车时控制收音机，这是更昂贵的汽车中已建立的功能。高分辨率的飞行时间相机和功能强大的嵌入式处理器通常构成这些手势识别系统的核心。但是，这带有价格标签。因此，我们调查了设计一种算法的可能性，该算法使用仅32x24像素的廉价低分辨率热摄像头预测手势，该算法足够轻巧，可以在低成本处理器上运行。我们录制了一个新的数据集，其中包括1300多个视频片段，用于培训和评估，并提出了一种轻质的低延迟预测算法。我们的最佳模型可实现95.9％的分类精度和83％的地图检测准确性，而其处理管道的延迟仅为一个帧。
Using hand gestures to answer a call or to control the radio while driving a car, is nowadays an established feature in more expensive cars. High resolution time-of-flight cameras and powerful embedded processors usually form the heart of these gesture recognition systems. This however comes with a price tag. We therefore investigate the possibility to design an algorithm that predicts hand gestures using a cheap low-resolution thermal camera with only 32x24 pixels, which is light-weight enough to run on a low-cost processor. We recorded a new dataset of over 1300 video clips for training and evaluation and propose a light-weight low-latency prediction algorithm. Our best model achieves 95.9% classification accuracy and 83% mAP detection accuracy while its processing pipeline has a latency of only one frame.
