行人
弹道
计算机科学
聚类分析
路径(计算)
人工智能
国家(计算机科学)
班级(哲学)
机器学习
算法
地理
天文
物理
考古
程序设计语言
作者
Hao Xue,Du Q. Huynh,Mark Reynolds
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2020-03-10
卷期号:32 (1): 77-90
被引量:49
标识
DOI:10.1109/tnnls.2020.2975837
摘要
Pedestrian path prediction is a very challenging problem because scenes are often crowded or contain obstacles. Existing state-of-the-art long short-term memory (LSTM)-based prediction methods have been mainly focused on analyzing the influence of other people in the neighborhood of each pedestrian while neglecting the role of potential destinations in determining a walking path. In this article, we propose classifying pedestrian trajectories into a number of route classes (RCs) and using them to describe the pedestrian movement patterns. Based on the RCs obtained from trajectory clustering, our algorithm, which we name the prediction of pedestrian paths by LSTM (PoPPL), predicts the destination regions through a bidirectional LSTM classification network in the first stage and then generates trajectories corresponding to the predicted destination regions through one of the three proposed LSTM-based architectures in the second stage. Our algorithm also outputs probabilities of multiple predicted trajectories that head toward the destination regions. We have evaluated PoPPL against other state-of-the-art methods on two public data sets. The results show that our algorithm outperforms other methods and incorporating potential destination prediction improves the trajectory prediction accuracy.
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