论文标题
Seeknet：改进的人类实例细分和通过加强学习基于优化的机器人搬迁的跟踪
SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation
论文作者
论文摘要
Amodal识别是系统检测遮挡对象的能力。大多数SOTA视觉识别系统都缺乏执行Amodal识别的能力。很少有研究通过被动预测或体现的识别方法实现了Amodal识别。但是，这些方法在现实世界中的挑战（例如动态障碍）中遇到了挑战。我们提出了Seeknet，这是一种通过体现的视觉识别来改进的氨摩尔足识别的优化方法。此外，我们为社交机器人实施Seeknet，在社会机器人中与拥挤的行人进行了多次互动。我们还证明了我们算法在遮挡人类检测和跟踪其他基准方面的好处。此外，我们与Seeknet建立了一个多机器人环境，以识别和跟踪拥挤地区空中疾病的视觉疾病标记。我们在模拟的室内环境中进行了实验，并表明我们的方法提高了Amodal识别任务的总体准确性，并且与基线方法相比，随着时间的推移，检测准确性的提高最大。
Amodal recognition is the ability of the system to detect occluded objects. Most SOTA Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or embodied recognition approaches. However, these approaches suffer from challenges in real-world applications, such as dynamic obstacles. We propose SeekNet, an improved optimization method for amodal recognition through embodied visual recognition. Additionally, we implement SeekNet for social robots, where there are multiple interactions with crowded pedestrians. We also demonstrate the benefits of our algorithm on occluded human detection and tracking over other baselines. Additionally, we set up a multi-robot environment with SeekNet to identify and track visual disease markers for airborne disease in crowded areas. We conduct our experiments in a simulated indoor environment and show that our method enhances the overall accuracy of the amodal recognition task and achieves the largest improvement in detection accuracy over time in comparison to the baseline approaches.
