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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking
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An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking

机译：异步神经形态事件驱动的基于视觉零件的形状跟踪

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摘要

Object tracking is an important step in many artificial vision tasks. The current state-of-the-art implementations remain too computationally demanding for the problem to be solved in real time with high dynamics. This paper presents a novel real-time method for visual part-based tracking of complex objects from the output of an asynchronous event-based camera. This paper extends the pictorial structures model introduced by Fischler and Elschlager 40 years ago and introduces a new formulation of the problem, allowing the dynamic processing of visual input in real time at high temporal resolution using a conventional PC. It relies on the concept of representing an object as a set of basic elements linked by springs. These basic elements consist of simple trackers capable of successfully tracking a target with an ellipse-like shape at several kilohertz on a conventional computer. For each incoming event, the method updates the elastic connections established between the trackers and guarantees a desired geometric structure corresponding to the tracked object in real time. This introduces a high temporal elasticity to adapt to projective deformations of the tracked object in the focal plane. The elastic energy of this virtual mechanical system provides a quality criterion for tracking and can be used to determine whether the measured deformations are caused by the perspective projection of the perceived object or by occlusions. Experiments on real-world data show the robustness of the method in the context of dynamic face tracking.
机译：对象跟踪是许多人工视觉任务中的重要步骤。当前的最新实现仍然在计算上仍然要求以高动态实时解决问题。本文提出了一种新颖的实时方法，用于从基于异步事件的摄像机的输出中基于视觉部分跟踪复杂对象。本文扩展了40年前由Fischler和Elschlager引入的图片结构模型，并介绍了该问题的新提法，允许使用常规PC在高分辨率下实时动态处理视觉输入。它依赖于将对象表示为由弹簧链接的一组基本元素的概念。这些基本元素由简单的跟踪器组成，这些跟踪器能够在常规计算机上成功跟踪几千赫兹的椭圆形目标。对于每个传入事件，该方法都会更新跟踪器之间建立的弹性连接，并实时保证与跟踪对象相对应的所需几何结构。这引入了高的时间弹性，以适应被跟踪物体在焦平面上的投影变形。该虚拟机械系统的弹性能为跟踪提供了质量标准，可用于确定测量的变形是由感知对象的透视投影还是由遮挡引起的。实际数据的实验表明，该方法在动态人脸跟踪的情况下具有鲁棒性。

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