An automatic bus route number recognition

Wongta, Pitchakorn; Kobchaisawat, Thananop; Chalidabhongse, Thanarat H.

doi:10.1109/jcsse.2016.7748910

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…When a bus arrives at the bus stop (Step 3), the application will inform users via voice announcement by smartphone speaker (Step 4). Although our concept is similar to previous researches [10][11][12][13][14], these methods had only two main steps, namely bus detection and bus-number recognition, as shown in Step 2 of Figure 1. In fact, blind users, in real situations, will hold their smartphones freely to take videos.…”

Section: Introductionmentioning

confidence: 88%

“…Overview of system design for bus identification using a smartphone camera. techniques [10][11][12][13][14]. Computer vision or image processing is useful and widely used for various applications, including bus identification for blind individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Computer vision or image processing is useful and widely used for various applications, including bus identification for blind individuals. For instance, the automatic Thai bus-number-recognition system [10] was proposed in 2016, and can segment and recognize bus-number digits with an accuracy of 73.47%. The problem of bus identification for the blind, not only in Thailand, has also piqued the interest of many researchers who live in other countries.…”

Section: Introductionmentioning

confidence: 99%

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Viewpoint Classification for the Bus-Waiting Blinds in Congested Traffic Environment

Tangsuksant

Noda

Kitagawa

et al. 2019

IJFIS

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To provide an effective notification service for the blinds awaiting the bus, it is crucial to have a viewpoint classification technique in which a viewpoint is defined with tilt and panning of camera. This paper proposes a viewpoint classification method using the car distribution information in the congested traffic environment. The proposed method takes four steps for classification. First, the YOLO algorithm is used to detect the car positions in the images. Second, the car positions are normalized for feature computation. Third, nineteen simple features are extracted and finally, the viewpoint classification is conducted. The proposed method uses the information gain measure to select relevant ones from the extracted features, and uses the Random Forest algorithm as a classifier. In the experiments, the proposed method has been tested for various roadside scenarios of congested traffic in day and night. The accuracies for car detection and viewpoint classification were 79.90% and 86.00%, respectively, which are improved compared to the prior work.

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Section: Introductionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Viewpoint Classification for the Bus-Waiting Blinds in Congested Traffic Environment

Tangsuksant

Noda

Kitagawa

et al. 2019

IJFIS

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“…Further, system maintenance is difficult when some part of the module is not working. On the other hand, the second system proposed is based on an image processing technique [12][13][14][15][16] to capture the bus numbers of the oncoming buses. Image processing is a fascinating technique that can be widely used for several applications, including bus identification for visually impaired people and blind individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Image processing is a fascinating technique that can be widely used for several applications, including bus identification for visually impaired people and blind individuals. For example, Pitchakorn Wongta et al [12], in 2016, proposed an automatic bus route number recognition system in Thailand.…”

Section: Introductionmentioning

confidence: 99%

Classification of Viewpoints Related to Bus-Waiting for the Assistance of Blind People

Tangsuksant¹,

Wada²

2018

International Journal of New Technology and Research

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Considering the current smartphone technology, blind individuals cannot detect oncoming buses from their smartphones. Consequently, this paper proposes the viewpoint classification for blind individuals during situations of non-congested traffic. The definition of ideal viewpoints for this research depends on two main factors, which are the suitable tilt and suitable panning of a smartphone by which it is possible to detect and recognize the bus number. This research aims to find the suitable panning of a smartphone, which related to suitable viewpoint, using the combined simplicity of image processing. The proposed concept for interpreting viewpoints is to extract essential features from the road area via images. These features comprise road length, vanishing point, and the percentage of road area. For this research, various roadside scenarios were tested under both daytime and nighttime illumination. The experimental outcome shows the successful classification of viewpoints with regard to the requirement of blind persons waiting for a bus on the roadside. These results were found to be robust and feasible for roadside scenes under various illumination conditions.

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A Wearable Assistive Device for Safe Travel Using Transfer Learning and IoT for Visually Impaired People

Priyatharshini¹,

Kumar²,

Sivakumar³

et al. 2021

Studies in Big Data

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An automatic bus route number recognition

Cited by 10 publications

References 8 publications

Viewpoint Classification for the Bus-Waiting Blinds in Congested Traffic Environment

Viewpoint Classification for the Bus-Waiting Blinds in Congested Traffic Environment

Classification of Viewpoints Related to Bus-Waiting for the Assistance of Blind People

A Wearable Assistive Device for Safe Travel Using Transfer Learning and IoT for Visually Impaired People

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