Abstract
Real-world applications with multiple sensors observing an event are expected to make continuously-available predictions, even in cases where information may be intermittently missing. We explore methods in ensemble learning and sensor fusion to make use of redundancy and information shared between four camera views, applied to the task of hand activity classification for autonomous driving. In particular, we show that a late-fusion approach between parallel convolutional neural networks can outperform even the best-placed single camera model. To enable this approach, we propose a scheme for handling missing information, and then provide comparative analysis of this late-fusion approach to additional methods such as weighted majority voting and model combination schemes.
Abstract (translated)
多个传感器观察事件的实际应用场景期望能够持续提供预测,即使信息可能间歇性缺失。我们探索了组合学习和传感器融合的方法,利用四个相机视图的冗余信息和共享的信息,应用于自主驾驶手部活动分类任务。特别是,我们表明,并行卷积神经网络之间的晚融合方法可以优于单个相机模型。为了实现这种方法,我们提出了一种处理缺失信息的方案,然后对晚融合方法与其他方法,如加权多数投票和模型组合方案进行了比较分析。
URL
https://arxiv.org/abs/2301.12592
