Bagheri Zahra M, Wiederman Steven D, Cazzolato Benjamin S, Grainger Steven, O'Carroll David C
Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia. School of Mechanical Engineering, The University of Adelaide, Adelaide, SA, 5005, Australia.
Bioinspir Biomim. 2017 Feb 16;12(2):025006. doi: 10.1088/1748-3190/aa5b48.
Robust and efficient target-tracking algorithms embedded on moving platforms, are a requirement for many computer vision and robotic applications. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. As inspiration, we look to biological lightweight solutions-lightweight and low-powered flying insects. For example, dragonflies pursue prey and mates within cluttered, natural environments, deftly selecting their target amidst swarms. In our laboratory, we study the physiology and morphology of dragonfly 'small target motion detector' neurons likely to underlie this pursuit behaviour. Here we describe our insect-inspired tracking model derived from these data and compare its efficacy and efficiency with state-of-the-art engineering models. For model inputs, we use both publicly available video sequences, as well as our own task-specific dataset (small targets embedded within natural scenes). In the context of the tracking problem, we describe differences in object statistics within the video sequences. For the general dataset, our model often locks on to small components of larger objects, tracking these moving features. When input imagery includes small moving targets, for which our highly nonlinear filtering is matched, the robustness outperforms state-of-the-art trackers. In all scenarios, our insect-inspired tracker runs at least twice the speed of the comparison algorithms.
对于许多计算机视觉和机器人应用来说，嵌入在移动平台上的强大且高效的目标跟踪算法是必不可少的。然而，即使有现代硬件的计算能力，部署实时系统仍然具有挑战性。作为灵感来源，我们将目光投向生物轻量化解决方案——体型轻巧、能量消耗低的飞行昆虫。例如，蜻蜓在杂乱的自然环境中追逐猎物和配偶，能在众多物体中巧妙地挑选出它们的目标。在我们的实验室中，我们研究了蜻蜓“小目标运动探测器”神经元的生理和形态，这些神经元可能是这种追逐行为的基础。在此，我们描述了基于这些数据得出的受昆虫启发的跟踪模型，并将其有效性和效率与最先进的工程模型进行比较。对于模型输入，我们既使用公开可用的视频序列，也使用我们自己特定任务的数据集（嵌入自然场景中的小目标）。在跟踪问题的背景下，我们描述了视频序列中物体统计信息的差异。对于一般数据集，我们的模型常常锁定在较大物体的小部分上，跟踪这些移动特征。当输入图像包含小的移动目标时，我们高度非线性的滤波与之匹配，其鲁棒性优于最先进的跟踪器。在所有场景中，我们受昆虫启发的跟踪器运行速度至少是比较算法的两倍。
