用姿态检测与序列模型预测行人过街意图，提升自动驾驶安全性。
Predicting Road Crossing Behaviour using Pose Detection and Sequence Modelling
- 结合姿态检测与三种序列模型进行行为预测。
- GRU在意图预测上表现最佳，1D CNN速度最快。
- 适用于自动驾驶中的行人行为预判场景。
全球正加速迈向基于人工智能的系统，自动驾驶车辆已在多地实现。这些车辆依赖传感器和摄像头识别物体并作出响应。因此，提前判断行人是否即将过马路至关重要。本研究聚焦于实验环境下预测行人过街意图，采用深度学习模型进行姿态检测，并结合序列建模进行时序预测。研究对比了三种序列建模方法，发现GRU在意图预测性能上优于LSTM，而1D CNN在处理速度上表现最佳。研究通过视频分析，将姿态检测输出与序列建模技术集成，构建端到端深度学习框架，用于预测行人过街意图。
原文摘要 · Abstract (English)
The world is constantly moving towards AI based systems and autonomous vehicles are now reality in different parts of the world. These vehicles require sensors and cameras to detect objects and maneuver according to that. It becomes important to for such vehicles to also predict from a distant if a person is about to cross a road or not. The current study focused on predicting the intent of crossing the road by pedestrians in an experimental setup. The study involved working with deep learning models to predict poses and sequence modelling for temporal predictions. The study analysed three different sequence modelling to understand the prediction behaviour and it was found out that GRU was better in predicting the intent compared to LSTM model but 1D CNN was the best model in terms of speed. The study involved video analysis, and the output of pose detection model was integrated later on to sequence modelling techniques for an end-to-end deep learning framework for predicting road crossing intents.
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