Abstract: The moving object detection with moving camera has the problems that it is difficult to model the background and the computation cost is usually high. In order to solve the problems, a method for detecting moving object with moving camera based on motion saliency was proposed, which realized accurate moving object detection and avoided complex background modeling. The moving objects were detected according to the saliency of the video scene, which was computed based on the simulation of the attention mechanism in human vision system and the moving properties of background and foreground when the camera moved in translation. Firstly, the motion features of object were extracted by optical flow method and the background motion texture was suppressed by 2-D Gaussian convolution. Then the global saliency of motion features was measured by counting the histogram. According to the temporal salient map, the color information of foreground and background was extracted respectively. Finally, Bayesian model was used to deal with temporal salient map for extracting salient moving objects. The experimental results on the public video datasets show that the proposed method can suppress background motion noise, while detecting the moving object distinctly and accurately in the dynamic scene with moving camera.

Key words: moving object detection, visual attention, saliency, optical flow method, Bayesian model

摘要： 针对移动镜头下的运动目标检测中的背景建模复杂、计算量大等问题,提出一种基于运动显著性的移动镜头下的运动目标检测方法,在避免复杂的背景建模的同时实现准确的运动目标检测。该方法通过模拟人类视觉系统的注意机制,分析相机平动时场景中背景和前景的运动特点,计算视频场景的显著性,实现动态场景中运动目标检测。首先,采用光流法提取目标的运动特征,用二维高斯卷积方法抑制背景的运动纹理;然后采用直方图统计衡量运动特征的全局显著性,根据得到的运动显著图提取前景与背景的颜色信息;最后,结合贝叶斯方法对运动显著图进行处理,得到显著运动目标。通用数据库视频上的实验结果表明,所提方法能够在抑制背景运动噪声的同时,突出并准确地检测出场景中的运动目标。

关键词: 运动目标检测, 视觉注意, 显著性, 光流法, 贝叶斯模型