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A Fast Mathematical Morphological Algorithm of Video-Based Moving Forklift Truck Detection in Noisy Environment
P. 57–65.
Chernousov V. O., Savchenko A.
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).
In book
Vol. 439. , Berlin: Springer, 2014.
Aleksei Samarin, Nazarenko A., Nazarenko D. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 10 Article 330
Automated microscopic image analysis is essential for quantitative microbiology, yet manual acquisition and generic recognition models struggle with morphological variability and scene artifacts. We address both ends of this pipeline. First, we present a low-cost, open-source acquisition system, based on an Arduino UNO R3 microcontroller and NEMA 17 stepper motors, that automates focusing, magnification, and ...
Added: October 14, 2026
Piskunova A., Korolev D., Скопин Н. Ю. et al., В кн.: ГрафиКон 2025 : материалы 35-й Международной конференции по компьютерной графике и машинному зрению (Россия, Йошкар-Ола, 30 сентября – 2 октября 2025 г.).: Йошкар-Ола: Поволжский государственный технологический университет, 2025. С. 659–669.
This article presents an overview of modern methods for solving the problem of recognizing handwritten content, including multilingual text (Russian and English) and graphical elements such as block diagrams consisting of rectangles and arrows, with a focus on the recognition of whiteboard images. For multilingual handwritten text, architectures capable of processing multiple languages are considered, ...
Added: October 9, 2026
Aleksei Samarin, Aleksei Toropov, Alexander Savelev et al., Pattern Recognition and Image Analysis 2024 Vol. 34 No. 4 P. 1044–1052
This paper presents a study aimed at improving the detection quality of small-sized microorganisms under challenging microscopic conditions through the application of a lightweight combined image preprocessing model. We focused on the task of detecting diplococci in images obtained through dynamic sample microscopy. The proposed approach employs predefined filters for image preprocessing, combined with the ...
Added: September 21, 2026
Aleksei Samarin, Aleksei Toropov, Alexander Savelev et al., , in: Pattern Recognition. ICPR 2024 International Workshops and Challenges. Kolkata, India, December 1, 2024, Proceedings, Part III. LNCS, volume 15616.: Cham: Springer, 2025. P. 308–320.
This research explores an innovative approach to enhancing the accuracy of detecting small microorganisms in complex microscopic environments. Our study introduces a streamlined, hybrid image pre-processing model specifically designed to address the challenges of identifying diplococci in live microscopy of dynamic samples. By integrating pre-defined filtering techniques with predictive adjustments for optimal applicability, our method ...
Added: September 21, 2026
Lebedev O. B., Черкасов Р. И., Известия ЮФУ. Технические науки 2025 № 5(247) С. 254–276
This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are ...
Added: September 10, 2026
Vasilev A., Kapitanov A., Roman Solovyev et al., PeerJ Computer Science 2026 Vol. 12 Article 3724
This article introduces MinMAE, a novel activation calibration method for Post-Training Quantization (PTQ) that significantly reduces accuracy loss in Convolutional Neural Networks (CNN). Motivated by the need for high-fidelity quantization without costly retraining, MinMAE directly minimizes the Mean Absolute Error (MAE) between original and dequantized activations, making it robust to outliers that degrade standard methods. ...
Added: May 3, 2026
Karpukhin I., Shipilov F., Savchenko A., Neurocomputing 2026 Vol. 672 Article 132771
Forecasting multiple future events within a given time horizon is essential for applications in finance, retail,
social networks, and healthcare. This problem is typically addressed using Marked Temporal Point Processes
(MTPP), which provide a principled framework for modeling both event timing and event labels. While most
existing research focuses on predicting only the next event, forecasting distant future ...
Added: February 25, 2026
Moshkin A., Лапутин Ф. А., Сидоров И. В., DIGITAL DIAGNOSTICS 2024 Т. 5 № S1 С. 40–42
BACKGROUND: Ovarian reserve reflects a woman's ability to successfully realize reproductive function. The assessment of ovarian reserve is an urgent task for clinical practice [1] and is important in scientific research. The use of computerized diagnostic image processing methods can accelerate and facilitate the performance of routine tasks in clinical practice. Their use in retrospective ...
Added: February 21, 2026
, in: Lecture Notes in Electrical EngineeringVol. 489: Applied Physics, System Science and Computers II.: Springer, 2019. P. 87–100.
To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API to modify and train a model for ...
Added: December 30, 2024
Kseniia Prokudina, Mikhail Skriplyonok, Alexander Vostrikov, , in: 2024 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), 20-24 May 2024.: IEEE, 2024. P. 865–869.
Added: November 26, 2024
Saleh H., Saleh S., Nathan Teyou Toure et al., , in: 2021 IEEE Concurrent Processes Architectures and Embedded Systems Virtual Conference (COPA).: IEEE, 2022. Ch. 7 P. 1–6.
The annual number of road deaths is still increasing, especially in less developed and developing countries. Road accidents are the 5th cause of death and the leading reason for death among young people between 5 and 29 years of age in 2030. In this study, a robust solution is implemented by integrating object recognition with ...
Added: October 31, 2022
Belykh M. Vladimirovna, Belov A. Vladimirovich, , in: 2022 International Conference on Interdisciplinary Research in Technology and Management, IRTM 2022 - Proceedings.: IEEE, 2022. P. 1–4.
Added: July 15, 2022
Savchenko A., Demochkin K., Grechikhin I., Pattern Recognition 2022 Vol. 121 Article 108248
In this paper, a user modeling task is examined by processing mobile device gallery of photos and videos. We propose a novel engine for preferences prediction based on scene recognition, object detection and facial analysis. At first, all faces in a gallery are clustered, and all private photos and videos with faces from large clusters ...
Added: August 19, 2021
Solovyev R. A., Telpukhov D. V., Romanova I. I. et al., , in: Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021.: IEEE, 2021. P. 2029–2034.
The paper proposes methodology for transferring architecture of modern neural network CenterNet to FPGA. CenterNet is a OneStage object detector that is used to detect and locate objects in images. Although this neural network has simple decoder, it shows good performance in terms of accuracy. Very high operation speed of the neural network hardware is ...
Added: August 8, 2021
Belov A. V., Belykh M., Tofayli S., , in: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTERDISCIPLINARY RESEARCH IN TECHNOLOGY AND MANAGEMENT (IRTM, 2021).: CRC Press, 2021. Ch. 32 P. 199–203.
The article deals with the problem of collecting information about railway traffic lights located on the territory of the Russian Federation. To solve this problem, a trained neural network is used that detects railway traffic lights in high-definition images. These images were taken by a camera installed on a train carriage, continuously photographing the area ...
Added: July 15, 2021
Rzaev E., Khanaev A., Amerikanov A., , in: 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2021. P. 719–723.
Added: July 4, 2021
Denis Zuenko, Makarov I., , in: Proceedings of the Conference on Modeling and Analysis of Complex Systems and Processes 2020 (MACSPro 2020)Vol. 2795.: CEUR Workshop Proceedings, 2020. P. 92–98.
In this paper, we study automatic recognition and counting of vehicles in the wild. For this problem, we tested several object detection models for car type recognition among five classes: Bicycle, Bus, Car, Motorcycle, Truck, Van. We extend existing dataset in order to balance classes and achieve classification quality for detected cars with 92% mAP ...
Added: January 4, 2021
Kornilov M., IEEE Signal Processing Letters 2020 Vol. 27 P. 1480–1484
We present a novel technique for estimating disk parameters (the center and the radius) from its 2D image. It is based on the maximal likelihood approach utilizing both edge pixels coordinates and the image intensity gradients. We emphasize the following advantages of our likelihood model. It has closed-form formulae for estimating the parameters, therefore, requiring ...
Added: November 2, 2020
Osokin A., Sumin D., Lomakin V., , in: Computer Vision – ECCV 2020; 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVVol. 12360.: NY: Springer, 2020. P. 635–652.
In this paper, we consider the task of one-shot object detection, which consists in detecting objects defined by a single demonstration. Differently from the standard object detection, the classes of objects used for training and testing do not overlap. We build the one-stage system that performs localization and recognition jointly. We use dense correlation matching ...
Added: October 28, 2020