Embedded real-time speed limit sign recognition using image processing and machine learning techniques

• Type: Article in International Scientific Journal
• Date: 2017-12

Authors

Samuel L. Gomes (Author) Other; Elizângela de S. Rebouças (Author) Other; Edson Cavalcanti Neto (Author) Other; João P. Papa (Author) Other; Victor H. C. Albuquerque (Author) Other; P. P. Rebouças Filho (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Neural Computing and Applications

Vol. 28

Pages: S573-S584

ISSN: 0941-0643

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 7 Citations

ISI Web of Science

Scientific classification

FOS: Medical and Health sciences

CORDIS: Technological sciences

Other information

Authenticus ID: P-00N-9PC

DOI: 10.1007/s00521-016-2388-3

Abstract (EN): The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 mu s to recognize a sign, while kNN took 11,721 ls and SVM 12,595 ls. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12