基于行人姿态的轨迹预测方法

In the field of autonomous driving, pedestrian trajectory prediction has been one of the research hotspots, and the uncertainty of pedestrian behavior poses a great challenge to trajectory prediction. Most of the current trajectory prediction methods only focus on the information interaction between pedestrians, ignoring the influence of pedestrian intention and other semantic information in the scene on the pedestrian trajectory. In order to achieve this, this paper suggests a method for predicting target pedestrian trajectory using pose keypoints based convolutional encoder-decoder network (PKCEDN). The method includes an attention mechanism that can learn the relationship between the current moment and past moment trajectories, as well as an encoder-decoder model based on convolutional, long and short-term memory (LSTM) networks. The proposed method has been tested on the MOT16, MOT17, and MOT20 public datasets, and the average error is reduced by about 36% compared to mainstream methods such as Linear, LSTM, Social-LSTM, Social-GAN, SR-LSTM, and M_sgtv, while ensuring no reduction in prediction speed.