Transformer Networks for Trajectory Classification

초록

Research related to Trajectory Classification is actively underway, and its application fields are also very diverse. Existing studies related to trajectory classification mainly used RNN-based models such as SimpleRNN, LSTM, GRU, etc. However, these Seq2Seq models cause a bottle neck problem that does not reflect all information when the length of the input sequence increases during the encoding process. Therefore, we propose a Transformer model for more accurate trajectory classification even in situations where the trajectory input sequence is long. As a dataset, we use MNIST stroke sequence dataset, which expresses the stroke of the numbers of the MNIST as a unit vector trajectory. As a result, Transformer achieved comparable performance to LSTM.

키워드