In this paper, we introduce HEADS-UP, the first egocentric dataset collected from head-mounted cameras, designed specifically for trajectory prediction in blind assistance systems. With the growing population of blind and visually impaired individuals, the need for intelligent assistive tools that provide real-time warnings about potential collisions with dynamic obstacles is becoming critical. These systems rely on algorithms capable of predicting the trajectories of moving objects, such as pedestrians, to issue timely hazard alerts. However, existing datasets fail to capture the necessary information from the perspective of a blind individual. To address this gap, HEADS-UP offers a novel dataset focused on trajectory prediction in this context. Leveraging this dataset, we propose a semi-local trajectory prediction approach to assess collision risks between blind individuals and pedestrians in dynamic environments. Unlike conventional methods that separately predict the trajectories of both the blind individual (ego agent) and pedestrians, our approach operates within a semi-local coordinate system, a rotated version of the camera's coordinate system, facilitating the prediction process. We validate our method on the HEADS-UP dataset and implement the proposed solution in ROS, performing real-time tests on an NVIDIA Jetson GPU through a user study. Results from both dataset evaluations and live tests demonstrate the robustness and efficiency of our approach.

翻译：本文介绍了HEADS-UP，首个从头戴式摄像头采集的第一人称数据集，专为盲人辅助系统中的轨迹预测而设计。随着盲人和视力障碍者人数的增长，对能够实时预警动态障碍物潜在碰撞的智能辅助工具的需求变得日益迫切。这类系统依赖于能够预测移动物体（如行人）轨迹的算法，以发出及时的危险警报。然而，现有数据集未能从盲人个体的视角捕捉必要信息。为填补这一空白，HEADS-UP提供了一个专注于此背景下轨迹预测的新型数据集。利用该数据集，我们提出了一种半局部轨迹预测方法，用于评估动态环境中盲人个体与行人之间的碰撞风险。与分别预测盲人个体（自我智能体）和行人轨迹的传统方法不同，我们的方法在半局部坐标系（相机坐标系的旋转版本）内操作，从而简化了预测过程。我们在HEADS-UP数据集上验证了我们的方法，并在ROS中实现了所提出的解决方案，通过用户研究在NVIDIA Jetson GPU上进行了实时测试。数据集评估和实时测试的结果均证明了我们方法的鲁棒性和高效性。