论文标题
在哪里和什么：基于驾驶员注意的对象检测
Where and What: Driver Attention-based Object Detection
论文作者
论文摘要
人驾驶员使用注意力机制专注于关键物体并在驾驶时做出决策。由于人们可以从凝视数据中揭示人类的注意力，因此近年来捕获和分析凝视信息已经出现，以使自动驾驶技术受益。在这种情况下，以前的工作主要旨在预测人类驾驶员在哪里看和缺乏对驾驶员关注的“什么”对象的知识。我们的工作弥合了像素级和对象级的注意力预测之间的差距。具体而言，我们建议将注意力预测模块整合到验证的对象检测框架中，并以基于网格的样式预测注意力。此外，基于预测的到处区域确认关键物体。我们在两个驾驶员注意数据集（BDD-A和DR（EYE）VE）上评估了我们提出的方法。我们的框架在像素级和对象级的注意力预测中实现了竞争性的最新性能，但计算中的效率要高得多（75.3 Gflops）。
Human drivers use their attentional mechanisms to focus on critical objects and make decisions while driving. As human attention can be revealed from gaze data, capturing and analyzing gaze information has emerged in recent years to benefit autonomous driving technology. Previous works in this context have primarily aimed at predicting "where" human drivers look at and lack knowledge of "what" objects drivers focus on. Our work bridges the gap between pixel-level and object-level attention prediction. Specifically, we propose to integrate an attention prediction module into a pretrained object detection framework and predict the attention in a grid-based style. Furthermore, critical objects are recognized based on predicted attended-to areas. We evaluate our proposed method on two driver attention datasets, BDD-A and DR(eye)VE. Our framework achieves competitive state-of-the-art performance in the attention prediction on both pixel-level and object-level but is far more efficient (75.3 GFLOPs less) in computation.
