In visual surveillance, robust foreground object detection is an essential step for further processing such as segmentation, tracking, and extraction of a scene's contextual information. Typical approaches continuously update background images and use then for detecting foreground objects. They involve many parameters that should be adjusted according to the situation where surveillance cameras are operating. We propose an algorithm for the robust detection of foreground objects using multiple difference images that requires only one parameter to adjust. We show that the proposed algorithm gives comparable results with less computation time through experimental results using test images with groundtruths.
This study shows how a mobile service robot can avoid obstacles, and presents a VPH method using feature information
for Local Path Planning. It is not easy to make a mobile service robot automatically move towards the goal. Path Planning
lays out the path through which a robot follows to reach the goal. It can be divided into two folds: Global Path Planning
(GPP) and Local Path Planning (LPP). Local path planning sets a path in a changing environment with moving obstacles
such as in a museum and exhibition hall so that the robot reaches the goal without any collision. This study evaluates the
Fusion Map-VPH (FM-VPH) Local path planning method with improved VPH by making use of the combined data drawn
up through the ultrasonic sensor and laser sensor and by means of feature information. The results of the simulations and
experiments have verified the validity of the methods described.