Mishra Abhishek, Ghosh Rohan, Principe Jose C, Thakor Nitish V, Kukreja Sunil L
Singapore Institute for Neurotechnology, National University of Singapore Singapore, Singapore.
Department of Electrical and Computer Engineering, University of Florida Gainesville, FL, USA.
Front Neurosci. 2017 Mar 3;11:83. doi: 10.3389/fnins.2017.00083. eCollection 2017.
Motion segmentation is a critical pre-processing step for autonomous robotic systems to facilitate tracking of moving objects in cluttered environments. Event based sensors are low power analog devices that represent a scene by means of asynchronous information updates of only the dynamic details at high temporal resolution and, hence, require significantly less calculations. However, motion segmentation using spatiotemporal data is a challenging task due to data asynchrony. Prior approaches for object tracking using neuromorphic sensors perform well while the sensor is static or a known model of the object to be followed is available. To address these limitations, in this paper we develop a technique for generalized motion segmentation based on spatial statistics across time frames. First, we create micromotion on the platform to facilitate the separation of static and dynamic elements of a scene, inspired by human saccadic eye movements. Second, we introduce the concept of as a methodology to partition spatio-temporal event groups, which facilitates computation of scene statistics and characterize objects in it. Experimental results show that our algorithm is able to classify dynamic objects with a moving camera with maximum accuracy of 92%.
运动分割是自主机器人系统的关键预处理步骤，有助于在杂乱环境中跟踪移动物体。基于事件的传感器是低功耗模拟设备，通过仅在高时间分辨率下对动态细节进行异步信息更新来表示场景，因此所需计算量显著减少。然而，由于数据异步性，使用时空数据进行运动分割是一项具有挑战性的任务。使用神经形态传感器进行目标跟踪的先前方法在传感器静止或有要跟踪物体的已知模型时表现良好。为了解决这些限制，在本文中，我们开发了一种基于跨时间帧空间统计的广义运动分割技术。首先，受人类眼球跳动的启发，我们在平台上创建微运动，以促进场景中静态和动态元素的分离。其次，我们引入了“[此处原文缺失相关概念内容]”的概念，作为划分时空事件组的一种方法，这有助于计算场景统计信息并对其中的物体进行特征描述。实验结果表明，我们算法能够以移动相机对动态物体进行分类，最高准确率达到92%。
