License plate recognition with hierarchical temporal memory model

Bolotova, Yu. A.; Druki, A.A.; Spitsyn, V.G.

doi:10.1109/ifost.2014.6991089

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…The application of HTM for anomaly detection and prediction has shown promising results [41], [47]. HTM has been applied in disease diagnosis [48], pattern recognition (signed polish words) [49], image processing (license plate recognition) [50]. Moreover, HTM also has been used in geospatial tracking applications (modelling the movements of objects) that detect anomalies in travel patterns [51].…”

Section: A Hierarchical Temporal Memory (Htm)mentioning

confidence: 99%

Reactive and Proactive Anomaly Detection in Crowd Management Using Hierarchical Temporal Memory

2022

IJMLC

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An effective crowd management system offers immediate reactive or proactive handling of potential hot spots, including overcrowded situations and suspicious movements, which mitigate or avoids severe incidents and fatalities. The crowd management domain generates spatial and temporal resolution that demands diverse sophisticated mechanisms to measure, extract and process the data to produce a meaningful abstraction. Crowd management includes modelling the movements of a crowd to project effective mechanisms that support quick emersion from a dangerous and fatal situation. Internet of Things (IoT) technologies, machine learning techniques and communication methods can be used to sense the crowd characteristic /density and offer early detection of such events or even better prediction of potential accidents to inform the management authorities. Different machine learning methods have been applied for crowd management; however, the rapid advancement in deep hierarchical models that learns from a continuous stream of data has not been fully investigated in this context. For example, Hierarchical Temporal Memory (HTM) has shown powerful capabilities for application domains that require online learning and modelling temporal information. This paper proposes a new HTM-based framework for anomaly detection in a crowd management system. The proposed framework offers two functions: (1) reactive detection of crowd anomalies and (2) proactive detection of anomalies by predicting potential anomalies before taking place. The empirical evaluation proves that HTM achieved 94.22%, which outperforms k-Nearest Neighbor Global Anomaly Score (kNN-GAS) by 18.12%, Independent Component Analysis-Local Outlier Probability (ICA-LoOP) by 18.17%, and Singular Value Decomposition Influence Outlier (SVD-IO) by 18.12%, in crowd multiple anomaly detection. Moreover, it demonstrates the ability of the proposed alerting framework in predicting potential crowd anomalies. For this purpose, a simulated crowd dataset was created using MassMotion crowd simulation tool.

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Section: A Hierarchical Temporal Memory (Htm)mentioning

confidence: 99%

Reactive and Proactive Anomaly Detection in Crowd Management Using Hierarchical Temporal Memory

2022

IJMLC

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“…Closed loops should be found on the image to detect the area of the character locations. The closed loops found on the image are highlighted by rectangular areas for further segmentation [9] (Fig. 6).…”

Section: Classifier Training In Detecting the Location Area Of Cmentioning

confidence: 99%

Development of multistage algorithm for text objects recognition in images

Cherneta

Druki

Spitsyn

2016

2016 International Siberian Conference on Control and Communications (SIBCON)

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In line with 4th industrial revolution (Industry 4.0), the mechatronics and related areas are fundamental to boost the developments of industry digitalization. However, it should not be forgotten that artificial intelligence (AI) has a great preponderance on the development of autonomous and intelligent systems incorporating the advances in mechatronics systems. It is common in different industries the need to identify and recognize products or objects for different purposes such as counts, quality control, selection of objects, among others. For these reasons, pattern recognition is increasingly being used in systems on the shop floor, usually implemented in computer vision systems with image processing in real time. This work focuses on automatic detection and text recognition in unstructured images for use on shop floor mechatronic systems with vision systems, to identify and recognize patterns in products. Unstructured images are images that does not have a pre-defined image model or is not organized in a predefined manner. Which means that there is no predefined calibration model, the system must identify and learn by itself to recognize the text patterns. The techniques of character recognition, also known as OCR (Optical Character Reader), are not new in the industry, however the use of machine learning algorithms together with the existing techniques of OCR, allow endow the systems of greater intelligence in the patterns recognition. The results achieved throughout the paper, demonstrates that it is possible to identify and recognize text in objects based on unstructured images with a high level of accuracy and that these algorithms can be used in real time applications.

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“…Its applicability is much wider than that of the general single scene recognition algorithm. The recognition algorithm [13] proposed by Bolotava Y A et al integrates many technologies, such as image filtering, connected component analysis, and memory model recognition. The main process of the algorithm is to collect the target image area with a binarization scheme, and then use the connected component analysis and memory model recognition technology to recognize the collected image at the character level, which has achieved good recognition results.…”

Section: Introductionmentioning

confidence: 99%

Research on License Plate Recognition Algorithm in Dark-light Environment Based on Deep Learning

Bai

2023

HSET

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The advent of intelligent transportation technology has witnessed substantial advancements in license plate recognition technology, thereby assuming a crucial role in contemporary traffic monitoring. Nevertheless, conventional license plate recognition technology is inherently constrained by lighting conditions, severely impeding its nocturnal application. Given the escalating demand for license plate recognition technology in nighttime traffic surveillance, traffic violation documentation, and parking facilities, there exists a significant scholarly impetus to investigate nighttime license plate recognition technology. This study primarily focuses on nocturnal license plate recognition and employs deep learning techniques to execute comprehensive license plate recognition tasks. By leveraging this approach, the accuracy of license plate recognition in low-light environments can be substantially enhanced, thereby fortifying nocturnal traffic supervision and augmenting vehicular safety levels.

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License plate recognition with hierarchical temporal memory model

Cited by 9 publications

References 11 publications

Reactive and Proactive Anomaly Detection in Crowd Management Using Hierarchical Temporal Memory

Reactive and Proactive Anomaly Detection in Crowd Management Using Hierarchical Temporal Memory

Development of multistage algorithm for text objects recognition in images

Research on License Plate Recognition Algorithm in Dark-light Environment Based on Deep Learning

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