Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-based cameras, these sensors capture a stream of asynchronous 'events' that pose multiple advantages over the former, like high dynamic range, low latency, low power consumption, and reduced motion blur. However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution. Moreover, as event-based cameras can only capture the relative changes in brightness of a scene, event data do not contain usual visual information (like texture and color) as available in video data from normal cameras. So, moving object detection in event-based cameras becomes an extremely challenging task. In this paper, we present an unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data (GSCEventMOD). We additionally show how the optimum number of moving objects can be automatically determined. Experimental comparisons on publicly available datasets show that the proposed GSCEventMOD algorithm outperforms a number of state-of-the-art techniques by a maximum margin of 30%.
翻译:移动物体探测是计算机视野中讨论其广泛应用的核心问题,例如自驾驶汽车、视频监视、安全和执行等。神经地貌视觉传感器(NVS)是模拟人类眼睛运行的生物感应器。与传统的基于框架的相机不同,这些传感器捕捉了对前者具有多重优势的不同步“活动”流,如高动态范围、低悬浮、低电耗和减少运动模糊。然而,这些优势是高成本的,因为事件相机数据通常含有更多噪音和分辨率低。此外,由于事件相机只能捕捉场景亮度的相对变化,事件数据并不包含正常相机视频数据中通常的视觉信息(像样的纹理和颜色)。因此,在基于事件相机中移动物体探测对前者具有多重优势,例如高动态范围、低悬浮度、低电耗和减少运动模糊。在基于事件的数据中移动物体探测的图形谱系技术(GSCEvenMOD),我们进一步展示了移动物体的最大比值数,通过常规的GSALA系统自动测定确定。