Moving Object Detection for Event-based Vision using Graph Spectral Clustering
Moving Object Detection for Event-based Vision using Graph Spectral Clustering
复制标题
使用图谱聚类进行基于事件的视觉的移动物体检测
DOI：
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复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Chowdhury
中科院分区：
文献类型：
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作者：
Anindya Mondal;R. Shashant;Jhony H. Giraldo;T. Bouwmans;A. Chowdhury
运动目标检测一直是计算机视觉中讨论的中心话题，因为其广泛的应用，如自动驾驶汽车，视频监控，安全和执法。神经形态视觉传感器（NVS）是模仿人眼工作的生物启发传感器。与传统的基于帧的相机不同，这些传感器捕获异步“事件”流，与前者相比具有多个优势，如高动态范围，低延迟，低功耗和减少运动模糊。然而，这些优点是以高成本为代价的，因为事件相机数据通常包含更多噪声并且具有低分辨率。此外，由于基于事件的相机只能捕获场景亮度的相对变化，因此事件数据不包含普通相机视频数据中可用的常见视觉信息（如纹理和颜色）。因此，基于事件的摄像机中的运动目标检测成为一项极具挑战性的任务。在本文中，我们提出了一个无监督的图谱聚类技术的运动目标检测在基于事件的数据（GSCEventMOD）。我们还展示了如何自动确定移动对象的最佳数量。在公开数据集上的实验比较表明，所提出的GSCEventMOD算法优于一些最先进的技术，最大保证金为30%。
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%.
DOI：
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发表时间：
2019-10
期刊：
ArXiv
影响因子：
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作者：
Yin Bi;Aaron Chadha;Alhabib Abbas;Eirina Bourtsoulatze;Y. Andreopoulos
通讯作者：
Yin Bi;Aaron Chadha;Alhabib Abbas;Eirina Bourtsoulatze;Y. Andreopoulos