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.
翻译:本文介绍了对机载摄像系统行人轨轨迹预测的新方法,该方法利用从视觉观察中可以推断到行人的行为特征。我们建议的方法称为“行为-Aware Pedistrian轨迹预测”(BA-PTP),处理多种输入模式,即行人捆绑箱、身体和头部方向及其外形,并配有独立的编码流。每条溪流的编码都使用一种模式关注机制进行结合,最终嵌入,用于预测图像中的未来捆绑框。在对行人行为预测的两个数据集的实验中,我们展示了使用行为特征进行行人轨预测的好处,并评估了拟议编码战略的有效性。此外,我们还调查了不同行为特征与基于减缩研究的预测性能的关联性关系。