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.
翻译:个人跟踪机器人有许多应用,例如安全、老年人护理和社会化机器人。当一个人在统一人群中移动时,这种任务尤其具有挑战性。此外,尽管文献中报告的跟踪器取得了显著进步,但最先进的跟踪器在这种情景中几乎没有针对人。在这项工作中,我们侧重于通过开发一个强大和实时的可实时应用对象跟踪器,提高机器人对任务执行者的认识。我们推出了一个新的机器人跟踪系统,配有一个新的 RGB-D 跟踪器,即与RGB-D (DDD) 的深度跟踪器,能够应对统一人群环境带来的棘手挑战。我们的跟踪器利用变压器编码解码器结构,使用RGB 和深度信息来区分目标人与类似的分散器。大量全面试验和结果显示,我们的跟踪器在两个定量评价指标中表现较高,并证实其优于其他SOTA跟踪器。