Abstract
Real-time moving object detection in unconstrained scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource. In this paper, an optical flow based moving object detection framework is proposed to address this problem. We utilize homography matrixes to online construct a background model in the form of optical flow. When judging out moving foregrounds from scenes, a dual-mode judge mechanism is designed to heighten the system's adaptation to challenging situations. In experiment part, two evaluation metrics are redefined for more properly reflecting the performance of methods. We quantitatively and qualitatively validate the effectiveness and feasibility of our method with videos in various scene conditions. The experimental results show that our method adapts itself to different situations and outperforms the state-of-the-art methods, indicating the advantages of optical flow based methods.
Abstract (translated)
由于动态背景，改变前景外观和有限的计算资源，在无约束场景中的实时运动物体检测是一项艰巨的任务。在本文中，提出了一种基于光流的运动物体检测框架来解决这个问题。我们利用单应矩阵在线构建光流形式的背景模型。在判断场景中的移动前景时，双模判断机制旨在提高系统对具有挑战性的情况的适应性。在实验部分中，重新定义了两个评估指标，以更恰当地反映方法的性能。我们在各种场景条件下定量和定性地验证了我们的方法在视频中的有效性和可行性。实验结果表明，我们的方法适应不同的情况，优于最先进的方法，表明了基于光流的方法的优势。
URL
https://arxiv.org/abs/1807.04890
