基于行人姿态的轨迹预测方法

Pedestrian trajectory prediction method based on pedestrian pose

在自动驾驶领域,行人轨迹预测一直是研究热点之一,行人行为的不确定性给轨迹预测带来很大的挑战。目前大部分轨迹预测方法只专注于行人之间的信息交互,忽略了行人意图和场景中其他语义信息对行人轨迹的影响。为此,提出一种基于行人姿态的卷积编码器-解码器网络(PKCEDN)来预测目标行人轨迹的方法,所提方法包含基于卷积、长短时记忆(LSTM)网络的编码器-解码器模型和能够学习当前时刻与过去时刻轨迹相关性的注意力机制。所提方法在MOT16、MOT17和MOT20公开数据集上进行了相关测试,与Linear、LSTM、Social-LSTM、Social-生成对抗网络(GAN)、SR-LSTM和Msgtv等主流方法相比,在保证预测速度不降低的前提下,平均误差降低约36%。

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 Msgtv,while ensuring no reduction in prediction speed.