A Comparative evaluation of foreground / background segmentation algorithms

Download
2012
Pakyürek, Muhammet
Foreground Background segmentation is a process which separates the stationary objects from the moving objects on the scene. It plays significant role in computer vision applications. In this study, several background foreground segmentation algorithms are analyzed by changing their critical parameters individually to see the sensitivity of the algorithms to some difficulties in background segmentation applications. These difficulties are illumination level, view angles of camera, noise level, and range of the objects. This study is mainly comprised of two parts. In the first part, some well-known algorithms based on pixel difference, probability, and codebook are explained and implemented by providing implementation details. The second part includes the evaluation of the performances of the algorithms which is based on the comparison v between the foreground background regions indicated by the algorithms and ground truth. Therefore, some metrics including precision, recall and f-measures are defined at first. Then, the data set videos having different scenarios are run for each algorithm to compare the performances. Finally, the performances of each algorithm along with optimal values of their parameters are given based on f measure.

Suggestions

A comparative study on pose estimation algorithms using visual data
Çetinkaya, Güven; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2012)
Computation of the position and orientation of an object with respect to a camera from its images is called pose estimation problem. Pose estimation is one of the major problems in computer vision, robotics and photogrammetry. Object tracking, object recognition, self-localization of robots are typical examples for the use of pose estimation. Determining the pose of an object from its projections requires 3D model of an object in its own reference system, the camera parameters and 2D image of the object. Mo...
Improving edge detection using ıntersection consistency
Çiftçi, Serdar; Yarman Vural, Fatoş Tunay; Kalkan, Sinan; Department of Computer Engineering (2011)
Edge detection is an important step in computer vision since edges are utilized by the successor visual processing stages including many tasks such as motion estimation, stereopsis, shape representation and matching, etc. In this study, we test whether a local consistency measure based on image orientation (which we call Intersection Consistency - IC), which was previously shown to improve detection of junctions, can be used for improving the quality of edge detection of seven different detectors; namely, C...
Comparison of histograms of oriented optical flow based action recognition methods
Erciş, Fırat; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2012)
In the task of human action recognition in uncontrolled video, motion features are used widely in order to achieve subject and appearence invariance. We implemented 3 Histograms of Oriented Optical Flow based method which have a common motion feature extraction phase. We compute an optical flow field over each frame of the video. Then those flow vectors are histogrammed due to angle values to represent each frame with a histogram. In order to capture local motions, The bounding box of the subject is divided...
A Confidence Ranked Co-Occurrence Approach for Accurate Object Recognition in Highly Complex Scenes
Angın, Pelin (2013-01-01)
Real-time and accurate classification of objects in highly complex scenes is an important problem for the Computer Vision community due to its many application areas. While boosting methods with the sliding window approach provide fast processing and accurate results for particular object categories, they cannot achieve the desired performance for more involved categories of objects. Recent research in Computer Vision has shown that exploiting object context through relational dependencies between object ca...
Object recognition and segmentation via shape models
Altınoklu, Metin Burak; Ulusoy, İlkay; Tarı, Zehra Sibel; Department of Electrical and Electronics Engineering (2016)
In this thesis, the problem of object detection, recognition and segmentation in computer vision is addressed with shape based methods. An efficient object detection method based on a sparse skeleton has been proposed. The proposed method is an improved chamfer template matching method for recognition of articulated objects. Using a probabilistic graphical model structure, shape variation is represented in a skeletal shape model, where nodes correspond to parts consisting of lines and edges correspond to pa...
Citation Formats
M. Pakyürek, “A Comparative evaluation of foreground / background segmentation algorithms,” M.S. - Master of Science, Middle East Technical University, 2012.