通过细分跟踪与未来运动估计,应用于跟踪人机器人
Shenlu Jiang1, Runze Cui1, Runze Wei1
1School of Computer Science and Engineering, Macau University of Science and Technology, Macao, Macao SAR, China.
Frontiers in neurorobotics
|September 13, 2023
概括
这项研究引入了对机器人的实时跟踪细分,以便机器人能够准确地跟随人们. 这种新方法精确地跟踪个人使用像素级别的细分,并预测未来的运动,以更好的机器人导航.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 人跟踪对于服务机器人至关重要,视觉技术是环境理解的关键.
- 现有的追踪检测方法需要大量的数据集,并且容易受到环境噪音的影响.
研究的目的:
- 开发一种新的实时跟踪细分方法,用于强大的服务机器人.
- 通过使用像素级别细分和未来运动估计来提高跟踪准确性.
主要方法:
- 一次性细分跟踪神经网络,用于精确的前景细分,克服兴趣区域 (ROI) 的限制.
- 一个预先训练的分类锁层来限制特征异常值和一个歧视性关联过器来防止误识.
- 一个运动估计神经网络来预测目标的未来运动,用于机器人控制.
主要成果:
- 拟议的框架证明了VOT,LaSot,YouTube-VOS和戴维斯跟踪数据集的有效性.
- 实现了精确的像素级跟踪和准确的对目标个体的未来运动预测.
- 在室内环境中成功验证了长期的人跟踪任务.
结论:
- 实时按细分跟踪框架比传统的机器人跟踪方法有了显著的改进.
- 这种方法显示出在服务机器人中实际应用的巨大潜力,提高了它们与人类互动的能力.
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