Algorithm derivation and its embedded system realization of speed limit detection for multiple countries

Lin, Yu-Min; Chou, Ting; Vinay, M; Guo, Jiun-In

doi:10.1109/iscas.2016.7539114

Cited by 4 publications

(8 citation statements)

References 10 publications

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“…The process of detection and recognition of speed limit road signs [35] can be broadly classified into three stages namely, (i) speed limit signs detection, (ii) digit segmentation, (iii) digit recognition and that of detection and recognition of speed regulatory road signs [36] also into three stages such as, (i) speed regulatory signs detection, (ii) feature extraction, (iii) feature matching. Figure 2 depicts the proposed algorithm used in detection and recognition of the road signs.…”

Section: Traditional Digital Image Processing Methods To Detect and Recognize Road Signsmentioning

confidence: 99%

A Study on Traditional and CNN Based Computer Vision Sensors for Detection and Recognition of Road Signs with Realization for ADAS

Shivanna¹,

Chen²,

Wu³

et al. 2022

Artificial Intelligence

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Section: Traditional Digital Image Processing Methods To Detect and Recognize Road Signsmentioning

confidence: 99%

A Study on Traditional and CNN Based Computer Vision Sensors for Detection and Recognition of Road Signs with Realization for ADAS

Shivanna¹,

Chen²,

Wu³

et al. 2022

Artificial Intelligence

Self Cite

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“…According to the works of [34,35], learning methods are used to adapt the changes of movement and other characteristics such as geometric aspect and appearance of the tracked object. These methods are usually used adaptive tracked object trackers and detectors.…”

Section: Object Trackingmentioning

confidence: 99%

“…Moreover, the long short-term memory (LSTM) model also considers attention learning to improve the important features, such as read and write operations. MemTrack [34] and MemDTC [35] used the attentional LSTM-based memory network to update the target template during tracking. The temporal feature-based attention for visual tracking is introduced by FlowTrack [36], which considers temporal information for the target.…”

Section: Tracking With Attention Networkmentioning

confidence: 99%

Information Extraction and Object Tracking in Digital Video

2022

Artificial Intelligence

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The research on computer vision systems has been increasing every day and has led to the design of multiple types of these systems with innumerous applications in our daily life. The recent advances in artificial intelligence (AI), together with the huge amount of digital visual data available, have boosted vision system performance in several ways. Computer systems with digital cameras using the most recent AI techniques are intelligent to perceive and understand the visual world. However, there are still several open challenges in using computer vision at the level of the human eye, despite the existence of several systems that outperform humans in several specific tasks.Information extraction and visual object tracking are essential tasks in computer vision with a huge number of real-world applications including intelligent management of Web videos, events and object detection, human-computer interaction, augmented reality, autonomous vehicles, robotics, sports, video indexing and retrieval, and surveillance and security, among many others. The goal of information extraction is to efficiently extract useful data from the images recorded over time in the form of digital videos. Visual object tracking aims to estimate an unknown visual trajectory of a target when an initial position is given in a video frame. The exponential effort in the development of new algorithms for information extraction and visual object tracking in digital videos is confirmed by the amount of money invested in scientific projects and in the quantity and quality of the manuscripts published in a considerable number of journals and conferences worldwide on these topics. This book is a result of research done by several researchers and professionals who have highly contributed to the field of image processing. The main goal is to present recent advances in this hot topic. I would like to thank all the authors for their excellent contributions. This book contains eight chapters divided into three sections. Section 1 consists of four chapters focusing on the visual tracking problem. Section 2 includes three chapters focusing on information extraction from images. Section 3 includes one chapter on new advances in image sensors.I hope that readers of this book will find it interesting and informative, considering it a good tool for their research or projects.

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“…Rotation invariant binary pattern (RIBP) was used to extract features and ANN was used for traffic signs classification. Lin et al [38] proposed a low-multifaceted speed limit detection and recognition process which not only supported distinctive sorts of speed breaking point signs from different nations but also maintained a great identification rate under severe climates. However, their proposed system was only limited to the speed limit traffic signs.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

Islam¹,

Raj

Mujtaba³

2017

Symmetry

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Abstract:The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the wellmaintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

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Algorithm derivation and its embedded system realization of speed limit detection for multiple countries

Cited by 4 publications

References 10 publications

A Study on Traditional and CNN Based Computer Vision Sensors for Detection and Recognition of Road Signs with Realization for ADAS

A Study on Traditional and CNN Based Computer Vision Sensors for Detection and Recognition of Road Signs with Realization for ADAS

Information Extraction and Object Tracking in Digital Video

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

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