论文标题
机器人人在统一人群环境中关注
Robot Person Following in Uniform Crowd Environment
论文作者
论文摘要
人跟踪的机器人有许多应用程序，例如安全，老年护理和社交机器人。当该人在统一的人群中移动时，这样的任务尤其具有挑战性。同样，尽管文献报道的追踪器取得了重大进展，但在这种情况下，最先进的跟踪器几乎没有针对人。在这项工作中，我们专注于通过开发强大且实时适用的对象跟踪器来提高机器人对遵循任务的人的看法。我们介绍了一个新的机器人跟踪系统，该系统具有新的RGB-D跟踪器，具有RGB-D（DTRD）的深层跟踪器，这对统一人群环境引入的棘手挑战具有弹性。我们的跟踪器利用带有RGB和深度信息的变压器编码器编码器架构来区分目标人员与类似的分散术者。大量的全面实验和结果表明，我们的跟踪器在两个定量评估指标中具有较高的性能，并确认其优于其他SOTA跟踪器。
Person-tracking robots have many applications, such as in security, elderly care, and socializing robots. Such a task is particularly challenging when the person is moving in a Uniform crowd. Also, despite significant progress of trackers reported in the literature, state-of-the-art trackers have hardly addressed person following in such scenarios. In this work, we focus on improving the perceptivity of a robot for a person following task by developing a robust and real-time applicable object tracker. We present a new robot person tracking system with a new RGB-D tracker, Deep Tracking with RGB-D (DTRD) that is resilient to tricky challenges introduced by the uniform crowd environment. Our tracker utilizes transformer encoder-decoder architecture with RGB and depth information to discriminate the target person from similar distractors. A substantial amount of comprehensive experiments and results demonstrate that our tracker has higher performance in two quantitative evaluation metrics and confirms its superiority over other SOTA trackers.
