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Tracking Natural Trails with Swarm-based Visual Saliency

机译：使用基于群的视觉显着性跟踪自然路径

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摘要

This paper proposes a model for trail detection and tracking that builds upon the observation that trails are salient structures in the robot's visual field. Due to the complexity of natural environments, the straightforward application of bottom-up visual saliency models is not sufficiently robust to predict the location of trails. As for other detection tasks, robustness can be increased by modulating the saliency computation based on a priori knowledge about which pixel-wise visual features are most representative of the object being sought. This paper proposes the use of the object's overall layout as the primary cue instead, as it is more stable and predictable in natural trails. Bearing in mind computational parsimony and detection robustness, this knowledge is specified in terms of perception-action rules, which control the behavior of simple agents performing as a swarm to compute the saliency map of the input image. For the purpose of tracking, multiframe evidence about the trail location is obtained with a motion-compensated dynamic neural field. In addition, to reduce ambiguity between the trail and trail-like distractors, a simple appearance model is learned online and used to influence the agents' activity. Experimental results on a large data set reveal the ability of the model to produce a success rate on the order of 97% at 20 Hz. The model is shown to be robust in situations where previous models would fail, such as when the trail does not emerge from the lower part of the image or when it is considerably interrupted.
机译：本文提出了一种用于轨迹检测和跟踪的模型，该模型建立在对轨迹是机器人视野中的显着结构的观察的基础上。由于自然环境的复杂性，自下而上的视觉显着性模型的直接应用尚不足以预测路线的位置。至于其他检测任务，可以通过基于关于哪些像素级视觉特征最能代表所寻求对象的先验知识来调制显着性计算来提高鲁棒性。本文建议使用对象的整体布局作为主要提示，因为它在自然轨迹中更稳定且可预测。考虑到计算的简约性和检测的鲁棒性，这些知识是根据感知行为规则来指定的，感知行为规则控制着作为群计算简单的代理行为以计算输入图像的显着性图的行为。为了进行跟踪，使用运动补偿的动态神经场获得有关路径位置的多帧证据。此外，为减少线索和类似线索的干扰因素之间的歧义，可以在网上学习一个简单的外观模型，并将其用于影响特工的活动。在大型数据集上的实验结果表明，该模型在20 Hz时能够产生97％左右的成功率。在以前的模型可能会失败的情况下，例如当踪迹没有从图像的下部出现或被严重中断时，该模型被证明是健壮的。

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  • 来源
    《Journal of Robotic Systems》 |2013年第1期|64-86|共23页
  • 作者

    Pedro Santana; Luis Correia; Ricardo Mendonca; Nelson Alves; Jose Barata;

  • 作者单位

    CTS-New University of Lisbon, Caparica, Portugal and LabMAg-University of Lisbon, Lisbon, Portugal;

    LabMAg-University of Lisbon, Lisbon, Portugal;

    CTS-New University of Lisbon, Caparica, Portugal;

    IntRoSys, S.A., Caparica, Portugal;

    CTS-New University of Lisbon, Caparica, Portugal;

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  • 正文语种 eng
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