Ki-Sang Hong

Where Are the Ball and Players? Soccer Game Analysis with Color Based Tracking and Image Mosaick

Knowing the locations of the players and the ball on a ground field is important for soccer game analysis. Given an image sequence, we address three main problems: 1) ground field extraction, 2) player and ball tracking and team identification and 3) absolute player positioning. The region of ground field is extracted on the basis of color information, within which all the other processing is restricted. Players are tracked by template matching and Kalman filtering. Occlusion reasoning is done by color histogram back-projection. To find the location of a player, afield model is constructed and a transformation between the input image and the field model is computed using feature points. Otherwise, an image-based mosaicking technique is applied. Using this image-to-model transformation, the absolute positions and the trajectories of players on the field model are determined. We tested our method on real image sequences and the experimental results are given.

Road detection in spaceborne SAR images using a genetic algorithm

This paper presents a technique for the detection of roads in a spaceborne synthetic aperture radar (SAR) image using a genetic algorithm (GA). Roads in a spaceborne SAR image can be modeled as curvilinear structures that possess width. Curve segments, which represent the candidate positions for roads, are extracted from the image using a curvilinear structure detector, and the roads are accurately detected by grouping those curve segments. For this purpose, the authors designed a grouping method based on a GA, which is a global optimization method. They combined perceptual grouping factors with it and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process, which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. The authors applied their method to ERS-1 SAR and SIR-C/X-SAR images that have a resolution of about 30 m. The experimental results show that our method can accurately detect road networks as well as single-track roads and is much faster than a globally applied GA approach.

Physics-based 3D position analysis of a soccer ball from monocular image sequences

In this paper, we propose a method for locating 3D position of a soccer ball from monocular image sequence of soccer games. Toward this goal, we adopted ground-model-to-image transformation together with physics-based approach, that a ball follows the parabolic trajectory in the air. By using the transformation the heights of a ball can be easily calculated using simple triangular geometric relations given the start and the end position of the ball on the ground. Here the heights of a ball are determined in terms of a players height. Even if the end position of a ball is not given on the ground due to kicking or heading of a falling ball before it touches the ground, the most probable trajectory can be determined by searching based on the physical fact that the ball follows a parabolic trajectory in the air. We have tested and experimented with a real image sequence the results of which seem promising.

Calibration-free augmented reality in perspective

This paper deals with video-based augmented reality and proposes an algorithm for augmenting a real video sequence with views of graphics objects without metric calibration of the video camera by representing the motion of the video camera in projective space. A virtual camera, by which views of graphics objects are generated, is attached to a real camera by specifying image locations of the world coordinate system of the virtual world. The virtual camera is decomposed into calibration and motion components in order to make full use of graphics tools. The projective motion of the real camera recovered from image matches has the function of transferring the virtual camera and makes the virtual camera move according to the motion of the real camera. The virtual camera also follows the change of the internal parameters of the real camera. This paper shows the theoretical and experimental results of our application of nonmetric vision to augmented reality.

Color-texture segmentation using unsupervised graph cuts

This paper proposes a novel approach to color-texture segmentation based on graph cut techniques, which finds an optimal color-texture segmentation of a color textured image by regarding it as a minimum cut problem in a weighted graph. A new texture descriptor based on the texton theory is introduced to efficiently represent texture attributes of the given image. Then, the segmentation is formulated in terms of energy minimization with graph cuts, where color and texton features are modelled with a multivariate finite mixture model with an unknown number of components. Contrary to previous supervised graph cut approaches, our method finds minimum cuts using split moves in an unsupervised way. The segmentation result, including the number of segments, is determined during the split moves without user interaction. Thus, our method is called unsupervised graph cuts. Experimental results of color-texture segmentation using various images including the MIT VisTex datasets and the Berkeley datasets are presented and analyzed in terms of precision and recall to verify its effectiveness.

Affine Motion Based CMOS Distortion Analysis and CMOS Digital Image Stabilization

The CMOS image distortion is due to the rolling shutter in CMOS image sensors (CISs) and it can be more exaggerated when a CIS camera moves rapidly. Several methods have been proposed to remove CMOS distortions made by the translational motion. But, in this paper, we propose the affine motion based CMOS distortion correction method combined with digital image stabilization. To remove CMOS distortions due to the affine global image motion, CMOS distortion model is proposed to explain the effect of the affine global image motion on the CMOS distortion in CISs. To improve CMOS videos visuals, we propose CMOS digital image stabilization to remove the jittering motions in the new image sequence obtained by our correction method. In addition, to reduce the computational time and the outlier effect, a reliable feature selection method is proposed to be used in the affine global image motion estimation. In the experiment results, we show that the proposed CMOS distortion correction method is more general than previous ones. Also, we show that our stabilization method can improve CMOS videos visuals running in real-time.

About the self-calibration of a rotating and zooming camera: Theory and practice

This paper deals with the uniqueness of the self-calibration of a rotating and zooming camera theoretically. We assume that the principal point and the aspect ratio are fixed but the focal length changes as the camera moves. In this case, at least one inter-image homography is required in order to compute the internal calibration parameters as well as the rotation. We analyze the effects of the deviation of the principal point on the estimation of the focal length and the rotation. The more the camera changes its zoom, the larger the effects are, and the larger the rotation angle is, the smaller the effects are. Thus, we may take the image center as the principal point in practical applications. Experiments using real images are given.

Appearance-based nude image detection

We propose an appearance-based nude image detection system. The proposed system is novel in that shape information is used to classify the nude images, and detect small nude images in a large background image. First, the proposed system finds skin regions using texture characteristics of the human skin, which then generates the skin likelihood image. Since the skin likelihood image contains shape information as well as skin color information, we used the skin likelihood image as a high level feature to classify the nude images. The image feature vector (resized skin likelihood image) is used as an input to a nonlinear-SVM. Experimental results show that the proposed system can achieve an excellent classification performance. Additionally, the proposed system can detect small nude images in a large image.

Fast line segment grouping method for finding globally more favorable line segments

In this paper, we propose a new method for extracting line segments from edge images. Our method basically follows a line segment grouping approach. This approach has many advantages over a Hough transform based approach in practical situations. However, since the process of the conventional line segment grouping approach is purely local, it does not provide a mechanism for finding more favorable line segments from a global point of view. Our method overcomes the local nature of the conventional line segment grouping approach, while retaining most of its advantages, by incorporating the useful concept of the Hough transform based approach into the line segment grouping approach. Our method is fast and allows elementary line segments to be shared simultaneously by several line segments, and the degree of sharing is determined by a user-specified threshold. We performed a series of tests to compare the performance of our method with that of six other methods. Throughout the tests our method ranked in the top two of the tested methods both in detection rate and computation time.

Robust Image Mosaicing of Soccer Videos using Self-Calibration and Line Tracking

Abstract:In this paper we propose an accurate and robust image mosaicing method of soccer video taken from a rotating and zooming camera using line tracking and self-calibration. The mosaicing of soccer videos is not easy, because their playing fields are low textured and moving players are included in the fields. Our approach is to track line features on the playing fields. The line features are detected and tracked using a self-calibration technique for a rotating and zooming camera. To track line features efficiently, we propose a new line tracking algorithm, called camera parameter guided line tracking, which works even when the camera motion undergoes sudden changes. Since we do not need to know any model for scenes beforehand, the proposed algorithm can be easily extended to other video sources, as well as other sports videos. Experimental results show the accuracy and robustness of the algorithm. An application of mosaicing is also presented.