This paper presents a survey on the latest methods of moving object detection in video
sequences captured by a moving camera. Although many researches and excellent works …

Background subtraction is a widely used operation in the video surveillance, aimed at
separating the expected scene (the background) from the unexpected entities (the …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …

Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate
that motion will be exploited most effectively, if it is regarded over larger time windows …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

Unsupervised learning requires a grouping step that defines which data belong together. A
natural way of grouping in images is the segmentation of objects or parts of objects. While …

We present a novel off-line algorithm for target segmentation and tracking in video. In our
approach, video data is represented by a multi-label Markov Random Field model, and …

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for
future study. This volume demonstrates the power of the Markov random field (MRF) in …

Privacy is a serious concern when microdata need to be released for ad hoc analyses. The
privacy goals of existing privacy protection approaches (eg, k-anonymity and l-diversity) are …

Background subtraction algorithms define the background as parts of a scene that are at
rest. Traditionally, these algorithms assume a stationary camera, and identify moving objects …