People-following in a leader-follower scheme is considered as a major contemporary research problem in the domain of human-robot coexistence. The problem essentially requires human detection and tracking, and, in recent times, vision sensing based shoe detection has evolved as an effective mechanism for human detection. The problem gets more challenging when the environment becomes affected by photometric conditions like varying illumination, shadows, specularities, etc. Recently, a state-of-the-art fast image template matching algorithm, called photometric-invariant CFAsT-match (PICFAsT-match), has been specifically proposed for shoe detection based people-following in those challenging environments. In this approach, for determining the best shoe matching result corresponding to a frame, each single transformation included in a number of specified grids of general affine transformations is evaluated. However, in order to achieve a high speed real-life implementation of PICFAsT-match, every transformation is assessed by considering a handful number of pixels sampled randomly from the images. Research beyond PICFAsT-match reveals that the matching accuracy significantly relies on the quality of the random sampling. In this paper, we propose an improved variant of PICFAsT-match, called chaos based PICFAsT-match (CBPICFAsT-match), where a novel approach for this process of random sampling of pixels has been developed. This strategy is based on the state-time histories of multiple fractional order chaotic systems. Experimental results aptly demonstrate the superiority of the proposed algorithm, when compared to competing state-of-the-art algorithms, successfully employed for the said shoe detection purpose in human-robot coexisting environments.

中文翻译：

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基于混沌的随机采样，在人类共存环境中使用视觉传感器进行光度不变鞋子检测


领导者-追随者计划中的人员追随被认为是人类与机器人共存领域的一个主要当代研究问题。该问题本质上需要人体检测和跟踪，并且近年来，基于视觉传感的鞋子检测已发展成为人体检测的有效机制。当环境受到光度条件（例如变化的照明、阴影、镜面反射等）的影响时，这个问题变得更具挑战性。最近，一种最先进的快速图像模板匹配算法，称为光度不变 CFAsT 匹配（PICFAsT 匹配） ），专门针对那些具有挑战性的环境中基于鞋子检测的人员跟踪而提出。在该方法中，为了确定对应于帧的最佳鞋子匹配结果，评估一般仿射变换的多个指定网格中包括的每个单个变换。然而，为了实现 PICFAsT 匹配的高速实际实现，每个变换都是通过考虑从图像中随机采样的少量像素来评估的。 PICFAsT 匹配之外的研究表明，匹配精度很大程度上取决于随机采样的质量。在本文中，我们提出了一种改进的 PICFAsT 匹配变体，称为基于混沌的 PICFAsT 匹配（CBPICFAsT 匹配），其中开发了一种用于像素随机采样过程的新颖方法。该策略基于多个分数阶混沌系统的状态时间历史。实验结果恰当地证明了所提出的算法与竞争的最先进算法相比的优越性，成功地应用于人机共存环境中的上述鞋子检测目的。