Detection of Street-Level Traffic Panels Based on Cascaded Color Segmentation

Guo, Jiefeng; You, Rongxuan; Feng, Chao; Huang, Lianfen

doi:10.1109/iccse.2018.8468775

Cited by 2 publications

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“…We propose a cascaded color segmentation method to detect text-based traffic signs in the street scene images [18]. In González's method [17], some dark colored objects, which are similar to the blue objects, are retained.…”

Section: Cascaded Color Segmentation Methods For Street-level Trafmentioning

confidence: 99%

Mixed Vertical-and-Horizontal-Text Traffic Sign Detection and Recognition for Street-Level Scene

2020

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Much effort has been dedicated to text-based traffic sign detection and recognition. However, there are still two problems. First, unlike English traffic signs with only horizontal text, Chinese traffic signs have both horizontal and vertical text. To the best of our knowledge, there is nothing in the literature about simultaneous recognition of both horizontal and vertical text in Chinese text-based traffic signs. Second, most existing methods focus on wild and expressway scenes; few focus on street scenes. To solve these problems, we propose a mixed vertical-and-horizontal-text traffic sign detection and recognition algorithm for streetlevel scene. First, an effective combination of different red, green and blue components is used to distinguish the traffic signs from many objects of similar color in the very complex street scenes. Second, unlike English letters, the strokes of many Chinese characters are unconnected, which may result in that a character will be detected as two or more characters. Unlike the English text lines, which are only horizontal, the Chinese text lines on text-based traffic signs are usually both in horizontal and vertical directions. Our proposed method uses the position and structural information of the characters to form the text lines. A dataset of Chinese text-based traffic signs is collected. Experimental results indicate the effectiveness of the proposed method.

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Section: Cascaded Color Segmentation Methods For Street-level Trafmentioning

confidence: 99%

Mixed Vertical-and-Horizontal-Text Traffic Sign Detection and Recognition for Street-Level Scene

2020

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“…In the literature, the commonly suggested approach for TP (Traffic Panel) detection is based on color and shape segmentation techniques [27], [28]. Unfortunately, these latter do not meet the efficiency requirements of real-time applications.…”

Section: B Text-based Traffic Panels Datasetsmentioning

confidence: 99%

ATTICA: A Dataset for Arabic Text-Based Traffic Panels Detection

et al. 2021

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Detection and recognition of traffic panels and their textual information are important applications of advanced driving assistance systems (ADAS). They can significantly contribute in enhancing road safety by optimizing the driving experience. However, these tasks might face two major challenges. First, the lack of suitable and good quality datasets. Second, the in-feasibility of global standardization of traffic panels in terms of shape, color and language of the written text. Present research is intensively directed toward Latin text-based panels, while other scripts such as Arabic remain quiet undervalued. In this paper, we address this issue by introducing ATTICA a , a new open-source multi-task dataset, consisting of two main sub-datasets: ATTICA_Sign for traffic signs/panels detection and ATTICA_Text for Arabic text extraction/recognition. Our dataset gathers 1215 images with 3173 traffic panel boxes, 870 traffic sign boxes and 7293 Arabic text boxes. In order to examine the utility and advantages of our dataset, we adopt stateof-the-art deep learning based approaches. The conducted experiments show promising results confirming the valuable addition of our dataset in this field of research.

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Detection of Street-Level Traffic Panels Based on Cascaded Color Segmentation

Cited by 2 publications

References 0 publications

Mixed Vertical-and-Horizontal-Text Traffic Sign Detection and Recognition for Street-Level Scene

Mixed Vertical-and-Horizontal-Text Traffic Sign Detection and Recognition for Street-Level Scene

ATTICA: A Dataset for Arabic Text-Based Traffic Panels Detection

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