In multi-camera surveillance system, the importance of each camera differs from each other and needs to be identified. In this paper, we develop an Edge-based Foreground Block Detection (EFBD) method to find out changing (foreground) blocks and then determine the importance of cameras based on EFBD. We also use H.264 codec to develop a multi-camera surveillance system which provides functions o...

This paper proposes a novel method for detecting foreground objects in nonstationary complex environments containing moving background objects. We derive a Bayes decision rule for classification of background and foreground changes based on inter-frame color co-occurrence statistics. An approach to store and fast retrieve color co-occurrence statistics is also established. In the proposed metho...

In this paper, we propose using augmented hypotheses which consider objectness, foreground and compactness for salient object detection. Our algorithm consists of four basic steps. First, our method generates the objectness map via objectness hypotheses. Based on the objectness map, we estimate the foreground margin and compute the corresponding foreground map which prefers the foreground objec...

The “Tour into the Picture” (TIP) system allows a user to work in the two-dimensional space of a single photograph to achieve a three-dimensional reconstruction [5]. This reconstruction models the scene as a box, much like a diorama. The user places the vanishing point and an “inner rectangle” indicating the back wall of the box, along with rectangles indicating foreground objects. To reconstru...

Background modeling is important for video surveillance. In the paper, we present a novel background modeling algorithm based on probabilistic graphic model and Gibbs Sampling. The background is modeled at different resolution level by image pyramids. We develop a time dependent pyramidal Markov Random Field (MRF) to represent the state of foreground/background at each pixel in the pyramid. Bot...