Помехоустойчивый морфологический алгоритм обнаружения вилочного погрузчика на видео
Background: The problem of video-based detection of the moving forklift truck is explored. It is shown that the detection quality of the state-of-the-art local descriptors (SURF, SIFT, FAST, ORB) is not satisfactory if the resolution is low and the lighting is changed dramatically.
Methods: In this paper we propose to use a simple mathematical morphological algorithm to detect the presence of a cargo on the forklift truck. At first, the movement direction is estimated by the updating motion history image method and the front part of the moving object is obtained. Next, contours are detected and binary morphological operations in front of the moving object are used to estimate simple geometric features of empty forklift.
Results: Our experimental study shows that the best results are achieved if the bounding rectangles of empty forklift contours are used as an object validation rule. Namely, FAR and FRR of empty cargo detection is 7\% and 50\% lower than FAR and FRR of the FAST descriptor. The proposed method is much more resistant to the effect of additive noise. The average frame processing time for our morphological algorithm is 5 ms (compare with 35 ms. of FAST method)
Conclusions: The proposed morphological method is task specific and can be used only for forklift truck detection. Additional detection principles need to be added to adopt algorithm for other moving object detection in noisy environment.
On the informatics and the software sides the questions of practical security are linked to the unstructured information processing algorithms applicable for the video array frames obtained by cross platform registration systems. Compression solutions become crucially important when the temporal evolution of the video stream exceeds the traffic capacity of the communication network. The basic image processing approach we exploited is to maintain of the highest resolution degree for the main part of the object we survey (for example, a man’s face or figure) whilst minimizing the information traffic from the image background by its artificial substitution with a homogeneous color filling. This method allowed us to obtain a significant compression rate (up to 7000).
An ensemble of classifiers has been built to solve the problem of video image recognition. The paper offers a way to estimate the a posteriori probability of an image belonging to a particular class in the case of an arbitrary distance and nearest neighbor method. The estimation is shown to be equivalent to the optimal naive Bayesian estimate given Kullback-Leibler divergence being used as proximity measure. The block diagram of a video image recognition system is presented. The system features automatic adaptation of the list of images of identical objects which is fed to the committee machine input. The system is tested in face recognition task using popular data bases (FERET, AT&T, Yale) and the results are discussed.
Measures and functionals of global asymmetry of noisy and noisefree images are axiomatically introduced. Explicit expressions are obtained that make these functionals applicable for determining the symmetry axes of noisy images. It is shown that some asymmetry functionals are unstable against noise levels of images; i.e., the symmetry axis obtained using these functionals may deviate significantly if the signalto noise ratio is large. Sufficient and necessary conditions are obtained under which the symmetry axes calcu lated using asymmetry functionals remain unchanged.
We investigate the specific problem of machine vision, namely, video-based detection of the moving forklift truck. It is shown that the detection quality of the state-of-the-art local descriptors (SURF, SIFT, etc.) is not satisfactory if the resolution is low and the illumination is changed dramatically. In this paper, we propose to use a simple mathematical morphological algorithm to detect the presence of a cargo on the forklift truck. At first, the movement direction is estimated by the updating motion history image method and the front part of the moving object is obtained. Next, contours are detected and morphological operations in front of the moving object are used to estimate simple geometric features of empty forklift. In the experimental study it has been shown that the proposed method has 40% lower FAR and 27% lower FRR in comparison with conventional matching of local descriptors. Moreover, our algorithm is 7 times faster.
The problem of automatic detection of the moving forklift truck in video data is explored. This task is formulated in terms of computer vision approach as a moving object detection in noisy environment. It is shown that the state-of-the-art local descriptors (SURF, SIFT, FAST, ORB) are not characterized with satisfactory detection quality if the camera resolution is low, the lighting is changed dramatically and shadows are observed. In this paper we propose to use a simple mathematical morphological algorithm to detect the presence of a cargo on the forklift truck. Its first step is the estimation of the movement direction and the front part of the truck by using the updating motion history image. The second step is the application of Canny contour detection and binary morphological operations in front of the moving object to estimate simple geometric features of empty forklift. The algorithm is implemented with the OpenCV library. Our experimental study shows that the best results are achieved if the difference of the width of bounding rectangles is used as a feature. Namely, the detection accuracy is 78.7% (compare with 40% achieved by the best local descriptor), while the average frame processing time is only 5 ms (compare with 35 ms for the fastest descriptor).
The problem of management of the nonlinear object which is exposed to impact of uncontrollable indignations, is considered in a key of differential game. Synthesis of optimum managements is made with application of transformation of the nonlinear equation of initial object in the differential equation with the parameters depending on a condition. The square-law functional of quality allows to formulate synthesis conditions in the form of need of search of solutions of the equation of Rikkati. The solution of the equation of Rikkati with the parameters depending on a condition, is in a symbolical view with application of algebraic methods that allows to generalize a number of earlier published theoretical results, to receive rather constructive decisions in a number of statements of problems of management.
The article is based upon the fact that the growing demand for master data management systems has not yet produced a commonly accepted metodology for their design and development/ The article offers two mathematical models? that allow a master data management systems designer a way to formally describe their system before development and verify the system quality by measurements? unique to master data management systems.