论文标题
基于事件的动作识别的时间二进制表示
Temporal Binary Representation for Event-Based Action Recognition
论文作者
论文摘要
在本文中，我们提出了一种事件聚合策略，以通过传统的计算机视觉算法将事件摄像机的输出转换为可处理的帧。所提出的方法首先生成了中间二进制表示的序列，然后通过简单地应用二进制转换转换来将其无损地转化为紧凑的格式。该策略使我们能够将时间信息直接编码到像素值中，然后通过深度学习模型来解释。我们将我们的策略（称为时间二进制表示）应用于手势识别的任务，在流行的DVS128手势数据集中获得了最新的结果。为了强调与现有方法相比，提出的方法的有效性，我们还在更具挑战性的进行实验的条件下收集了数据集的扩展。
In this paper we present an event aggregation strategy to convert the output of an event camera into frames processable by traditional Computer Vision algorithms. The proposed method first generates sequences of intermediate binary representations, which are then losslessly transformed into a compact format by simply applying a binary-to-decimal conversion. This strategy allows us to encode temporal information directly into pixel values, which are then interpreted by deep learning models. We apply our strategy, called Temporal Binary Representation, to the task of Gesture Recognition, obtaining state of the art results on the popular DVS128 Gesture Dataset. To underline the effectiveness of the proposed method compared to existing ones, we also collect an extension of the dataset under more challenging conditions on which to perform experiments.
