Automation in experiments carried out on animals is getting more and more important in research. Computers take over laborious and time-consuming activities like recording and analysing images of the experiment scene. The first step in an image analysis is finding and distinguishing between the observed animals and then tracking all objects during the experiment. In this paper four tracking methods are presented. Quantitative and qualitative figures of merit are applied to confront those methods. The comparison takes into consideration the level of correct object recognition during different disturbances, the speed of computation, requirements as to the frame rate and image illumination, quality of recovering from occluded situations and others.

This paper proposes a method for offline accurate ball tracking for short volleyball actions in sport halls. Our aim is to detect block touches on the ball and to determinate accurate trajectory and impact positions of the ball to support referees. The proposed method is divided into two stages, namely training and ball tracking, and is based on background subtraction. Application of the Gaussian mixture model has been used to estimate a background, and a high-speed camera with a capture rate of 180 frames per second and a resolution of 1920 × 1080 are used for motion capture. In sport halls significant differences in light intensity occur between each sequence frame. To minimize the influence of these light changes, an additional model is created and template matching is used for accurate determination of ball positions when the ball contour in the foreground image is distorted. We show that this algorithm is more accurate than other methods used in similar systems. Our light intensity change model eliminates almost all pixels added to images of moving objects owing to sudden changes in intensity. The average accuracy achieved in the validation process is of 0.57 pixel. Our algorithm accurately determined 99.8% of all ball positions from 2000 test frames, with 25.4 ms being the average time for a single frame analysis. The algorithm presented in this paper is the first stage of referee support using a system of many cameras and 3D trajectories.

This article deals with the problem of finding the maximum number of maximum cliques in a weighted graph with all edges between vertices from different d-division of a graph with the minimum total weight of all these cliques, and the problem of finding the maximum number of maximum cliques in a nonweighted graph with not all edges between vertices from different d-division of the graph. This article presents new ant algorithms with new desire functions for these problems. These algorithms were tested for their purpose with different changing input parameters, the test results were tabulated and discussed, the best algorithms were indicated.