Noise Resistant Morphological Algorithm of Moving Forklift Truck Detection on Noisy Image Data
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.
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.
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.
The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
This work addresses the problem of video matting, that is extracting the opacity-layer of a foreground object from a video sequence. We introduce the notion of alpha-flow which corresponds to the flow in the opacity layer. The idea is derived from the process of rotoscoping, where a user-supplied object mask is smoothly interpolated between keyframes while preserving its correspondence with the underlying image. Our key contribution is an algorithm which infers both the opacity masks and the alpha-flow in an efficient and unified manner. We embed our algorithm in an interactive video matting system where the first and last frame of a sequence are given as keyframes, and additional user strokes may be provided in intermediate frames. We show high quality results on various challenging sequences, and give a detailed comparison to competing techniques.
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018. The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
We present a system for the large-scale automatic traffic signs recognition and mapping and experimentally justify design choices made for different components of the system. Our system works with more than 140 different classes of traffic signs and does not require labor -intensivelabellingof a large amount of training data due to the training on synthetically generated images. We evaluated our system on the large dataset of Russian traffic signs and made this dataset publically available to encourage futurecomparison.
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.
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).
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
It is well-known that the class of sets that can be computed by polynomial size circuits is equal to the class of sets that are polynomial time reducible to a sparse set. It is widely believed, but unfortunately up to now unproven, that there are sets in EXPNP, or even in EXP that are not computable by polynomial size circuits and hence are not reducible to a sparse set. In this paper we study this question in a more restricted setting: what is the computational complexity of sparse sets that are selfreducible? It follows from earlier work of Lozano and Torán (in: Mathematical systems theory, 1991) that EXPNP does not have sparse selfreducible hard sets. We define a natural version of selfreduction, tree-selfreducibility, and show that NEXP does not have sparse tree-selfreducible hard sets. We also construct an oracle relative to which all of EXP is reducible to a sparse tree-selfreducible set. These lower bounds are corollaries of more general results about the computational complexity of sparse sets that are selfreducible, and can be interpreted as super-polynomial circuit lower bounds for NEXP.