Prediction of Pedestrian Trajectory in a Crowded Environment Using RNN Encoder-Decoder

Deep learning is the current state-of-the-art technique for most machine learning and data analytics tasks. It has been applied to all aspects of human life. Traditional methods are not competitive for solving pedestrian trajectory prediction problems due to the diversity of its environment and the uncertainty of the original trajectory. Due to the great success of the RNN (Recurrent Neural Networks) architecture in sequence prediction, it has become the first choice to us for solving pedestrian trajectory prediction problems in a crowded environment. In this work, in order to solve the problem that RNN architecture does not have great accuracy when the input is long sequence, we use LSTM (Long Short Term Memory) which produces higher accuracy when the input is long sequence. In the process of building our LSTM based prediction model, we find the best loss function through experiments and data analysis. Then we try various model hyperparameters combinations and find best hyperparameter values and parameter ranges that would make the prediction results more accurate. At the same time, we creatively use velocity values of pedestrian trajectory rather than coordinate values as input of the model. Experiments on several datasets shows that the proposed approach achieves high accuracy when it predicts pedestrian trajectory in a crowded environment.