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Encoding bird's trajectory using Recurrent Neural Networks

机译：使用递归神经网络编码鸟类的轨迹

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Recurrent Neural Networks (RNNs) are currently state of art tools for processing and classifying data sequences. This work aims to exploit these capabilities in Long-Short Term Memory (LSTM) networks which are a powerful variant of RNNs for encoding the birds' trajectory data into state vectors. These vectors should encapsulate the contextual information about the immediate trajectory coordinates. Therefore, they can generate new trajectory points based on their state and the latest output. However, probabilistic behavior of birds, effects of environment and noisy nature of measurements pose challenges for training and testing of the LSTM network models. This study solely focuses on the effects of spatial context and their significance in subsequent outputs to achieve compact representation of the traversed trajectory. Therefore, trajectory coordinates of birds were used as input to LSTM networks to learn spatial path features encoded in hidden vectors of the network. In the end, t-SNE method is used to visualize the state vectors in lower dimensional space embeddings and It was observed that these vectors contained contextual information about the traversed path.
机译：递归神经网络（RNN）是当前用于处理和分类数据序列的先进工具。这项工作旨在利用长期记忆（LSTM）网络中的这些功能，该网络是RNN的强大变体，用于将鸟类的轨迹数据编码为状态向量。这些向量应封装有关即时轨迹坐标的上下文信息。因此，他们可以根据其状态和最新输出生成新的轨迹点。但是，鸟类的概率行为，环境的影响和测量的噪声性质对LSTM网络模型的训练和测试提出了挑战。这项研究仅关注空间上下文的影响及其在后续输出中的重要性，以实现遍历轨迹的紧凑表示。因此，将鸟类的轨迹坐标用作LSTM网络的输入，以学习在网络的隐藏向量中编码的空间路径特征。最后，使用t-SNE方法可视化低维空间嵌入中的状态向量，并观察到这些向量包含有关遍历路径的上下文信息。

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