Segmenting, Modeling, and Matching Video Clips Containing Multiple Moving Objects

SEGMENTING MOVING OBJECTS: THE Modest VIDEO OBJECT KERNEL

A system separating objects moving within a slow changing background is presented. The originality of the approach resides in two related components. First, the change detection robust to camera noise which does not require any sophisticated parametric tuning as it is based on a probabilistic method. Second, the change is detected between a video frame representing a scene at a given time, and ...

Video Deblurring and Super-Resolution Technique for Multiple Moving Objects

Video camera is now commonly used and demand of capturing a single frame from video sequence is increasing. Since resolution of video camera is usually lower than digital camera and video data usually contains a many motion blur in the sequence, simple frame capture can produce only low quality image; image restoration technique is inevitably required. In this paper, we propose a method to rest...

Estimation of Image Motion in Scenes Containing Multiple Moving Objects

Modeling of Moving Objects in a Video Database

Modeling moving objects has become a topic of increasing interest in the area of video databases. Two key aspects of such modeling are spatial and temporal relationships. In this paper we introduce an innovative way to represent the trajectory of a single moving object and the relative spatio-temporal relations between multiple moving objects. The representation supports a rich set of spatial t...

Unsupervised Learning of Multiple Aspects of Moving Objects from Video

A popular framework for the interpretation of image sequences is based on the layered model; see e.g. Wang and Adelson [8], Irani et al. [2]. Jojic and Frey [3] provide a generative probabilistic model framework for this task. However, this layered models do not explicitly account for variation due to changes in the pose and self occlusion. In this paper we show that if the motion of the object...