No abstract

CAMShift algorithm is a fast and simple color based tracking algorithm but it is not robust to camera motion because it faces a problem when the tracked object moves across regions of background with similar colors with tracked object. Some algorithms have been proposed to reduce this problem. All of these proposed methods assign a probability to pixels belonging to object. However, in this paper we assign a probability to regions instead of pixels. Assigning probability to regions leads to a more accurate algorithm than the method of assigning probability to pixels. In this paper we proposed a region based approach to CAMShift (RCAMShift). We first demonstrate the problem of CAMShift. Then we show how our approach can improve CAMShift algorithm especially in a situation where there are similar colors in background.

No abstract

Localization is an essential step in visual navigation algorithms in robotics. Some visual navigation algorithms define the environment through sequential images, which are called visual path. The interval between each consecutive image is called a segment. One crucial step in these kinds of navigation is to find the segment in which the robot is placed (segment selflocalization). Visual segment self-localization methods consist of two stages. In the first stage, a feature matching between the current image of the robot with all the images that form the visual path is done. Usually, in this stage, outliers removal methods such as RANSAC are used after matching to remove the outliers matched features. In the second stage, a segment is chosen depending on the results of the first one. Segment self-localization methods estimate a segment depending just on the percentage of the matched features. This leads to estimate the segment incorrectly in some cases. In this paper, another parameter also is considered to estimate the segment. The parameter is based on the perspective projection model. Moreover, instead of RANSAC which is a stochastic and time consuming method, a more straightforward and more effective method is proposed for outliers detection. The proposed methods are tested on Karlsruhe dataset, and acceptable results are obtained. Also, the methods are compared with three reviewed methods by Nguyen et al. (J Intell Robot Syst 84:217, 2016). Although the proposed methods use a more straightforward outlier method, they give more accurate results.