监控视频运动分割是视频浓缩、行为识别等视频智能处理的基础和前提,是计算机视觉领域的研究热点。现存运动分割方法大多步骤繁琐、计算量大,难以应用于计算能力有限的领域。为此,提出了一种联合二分思想和时空管道的监控视频运动分割方法。该方法首先使用嵌套椭圆时空管道模型计算初始累计时空流量来判断目标轨迹完整性(completeness of target trajectory,CTT)；然后结合二分思想动态地调节椭圆采样线,自适应地捕捉采样区域的运动目标；最后提取采样线上的全部像素点形成自适应时空管道进行运动分割。实验结果表明,所提方法在保证精 度的同时计算速度明显优于对比方法,且所提方法鲁棒性强,对运动情况多变的监控场景同样适用。

Motion segmentation of surveillance video is the basis and premise of intelligent video processing such as video synopsis and behavior recognition,and is a research hotspot in the field of computer vision.Nevertheless,existing motion segmentation methods usually suffer from cumbersome steps and mass calculation, and their application is restricted in the field of limited computing power.To address these issues,we propose a method called motion segmentation of surveillance video by combining dichotomous and spatio-temporal tube.Firstly,the initial spatio-temporal flow is calculated using nested elliptical spatio-temporal tube model to judge the completeness of target trajectory (CTT).Secondly,we adjust dynamically the elliptical sampling line by combining the bisection to capture adaptively the moving target in the sampling area.Finally,the pixels on the sampling line are extracted to form the adaptive spatio-temporal tube for motion segmentation.Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both computing speed and accuracy,which has strong robustness and is also suitable for surveillance scenarios with changing motion.

国家自然科学基金(61702347,62027801)、 中央引导地方科技发展资金项目(226Z0501G)、 河北省自然科学基 金(F2022210007,F2017210161)和河北省高等学校科学技术研究项目(ZD2022100,QN2017132) 资助项目