论文标题
使用行为特征的车载行人轨迹预测
On-Board Pedestrian Trajectory Prediction Using Behavioral Features
论文作者
论文摘要
本文提出了一种新颖的方法，用于对车载摄像头系统的行人轨迹预测，该方法利用了行人的行为特征，可以从视觉观察中推断出来。我们提出的方法称为行为感知的行人轨迹预测（BA-PTP），处理了多种输入方式，即行人的边界框，身体和头部方向以及姿势，并具有独立的编码流。每个流的编码都使用模态注意机制进行融合，从而导致最终嵌入，用于预测图像中未来的边界框。 在两个数据集的实验进行行人行为预测中，我们证明了将行为特征用于行人轨迹预测的好处，并评估了建议的编码策略的有效性。此外，我们根据消融研究研究了不同行为特征在预测绩效上的相关性。
This paper presents a novel approach to pedestrian trajectory prediction for on-board camera systems, which utilizes behavioral features of pedestrians that can be inferred from visual observations. Our proposed method, called Behavior-Aware Pedestrian Trajectory Prediction (BA-PTP), processes multiple input modalities, i.e. bounding boxes, body and head orientation of pedestrians as well as their pose, with independent encoding streams. The encodings of each stream are fused using a modality attention mechanism, resulting in a final embedding that is used to predict future bounding boxes in the image. In experiments on two datasets for pedestrian behavior prediction, we demonstrate the benefit of using behavioral features for pedestrian trajectory prediction and evaluate the effectiveness of the proposed encoding strategy. Additionally, we investigate the relevance of different behavioral features on the prediction performance based on an ablation study.
