Person Following Robot Using Selected Online Ada-Boosting with Stereo Camera

2017 14th Conference on Computer and Robot Vision (CRV) Pub Date : 2017-05-16 DOI:10.1109/CRV.2017.55

B. Chen, Raghavender Sahdev, John K. Tsotsos

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引用次数: 45

Abstract

Person following behavior is an important task for social robots. To enable robots to follow a person, we have to track the target in real-time without critical failures. There are many situations where the robot will potentially loose tracking in a dynamic environment, e.g., occlusion, illumination, pose-changes, etc. Often, people use a complex tracking algorithm to improve robustness. However, the trade-off is that their approaches may not able to run in real-time on mobile robots. In this paper, we present Selected Online Ada-Boosting (SOAB) technique, a modified Online Ada-Boosting (OAB) tracking algorithm with integrated scene depth information obtained from a stereo camera which runs in real-time on a mobile robot. We build and share our results on the performance of our technique on a new stereo dataset for the task of person following. The dataset covers different challenging situations like squatting, partial and complete occlusion of the target being tracked, people wearing similar clothes, appearance changes, walking facing the front and back side of the person to the robot, and normal walking.

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基于立体摄像头的人跟踪机器人选择在线数据增强

人的行为跟踪是社交机器人的一项重要任务。为了使机器人能够跟随人，我们必须在没有严重故障的情况下实时跟踪目标。在许多情况下，机器人在动态环境中可能会失去跟踪，例如，遮挡，照明，姿势变化等。通常，人们使用复杂的跟踪算法来提高鲁棒性。然而，代价是他们的方法可能无法在移动机器人上实时运行。在本文中，我们提出了一种改进的在线Ada-Boosting (OAB)跟踪算法，该算法集成了实时运行在移动机器人上的立体摄像机获取的场景深度信息。我们建立并分享了我们的技术在一个新的立体数据集上的性能结果，用于人跟随任务。该数据集涵盖了不同的具有挑战性的情况，如蹲着、被跟踪目标的部分和完全遮挡、穿着相似衣服的人、外表变化、面向机器人的人的前后侧行走以及正常行走。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 14th Conference on Computer and Robot Vision (CRV)

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