A Framework for Real-time Traffic Sign Detection and Recognition using Grassmann Manifolds

Gupta, Any; Choudhary, Ayesha

doi:10.1109/itsc.2018.8569556

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…The comparison was carried out in two-lane mode, which the lane detection only applied to the two boundaries of the host lane on Caltech lanes dataset. VBLD results were compared with the lane detection methods described by Aly (2008); Ye et al (2018); Gupta and Choudhary (2018); Hoang et al (2017); Kim et al (2017); Niu et al (2016); Hou et al (2016); Shin et al (2015); Guo et al (2015); Liu and Li (2013); Ruyi et al (2011) under Caltech lanes dataset. The main reason for selecting Aly (2008) works is because Aly generated the public Caltech lanes dataset for evaluation and most of the researchers evaluated performance of their lane detection method using this dataset.…”

Section: Resultsmentioning

confidence: 99%

“…As shown in Table 11, the average lane detection rate of VBLD was 8% lower than the state-of-the-art lane detection method in Kim et al (2017) and most of the lane detection methods in Table 11. It is mostly due to state-of-the-art lane detection methods used sophisticated processing environment such as higher processor clock speed per core count and some methods like Gupta and Choudhary (2018); Hou et al (2016); Shin et al (2015); Ruyi et al (2011) applied tracking stage after detection stage for enhancing lane detection rate. However, VBLD outperforms most of the state-of-the-art lane detection methods in runtime evaluation.…”

Section: Resultsmentioning

confidence: 99%

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Vision-based lane departure warning framework

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Hossen

Imaduddin

et al. 2019

Heliyon

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Collisions arising from lane departures have contributed to traffic accidents causing millions of injuries and tens of thousands of casualties per year worldwide. Many related studies had shown that single vehicle lane departure crashes accounted largely in road traffic deaths that results from drifting out of the roadway. Hence, automotive safety has becoming a concern for the road users as most of the road casualties occurred due to driver's fallacious judgement of vehicle path. This paper proposes a vision-based lane departure warning framework for lane departure detection under daytime and night-time driving environments. The traffic flow and conditions of the road surface for both urban roads and highways in the city of Malacca are analysed in terms of lane detection rate and false positive rate. The proposed vision-based lane departure warning framework includes lane detection followed by a computation of a lateral offset ratio. The lane detection is composed of two stages: pre-processing and detection. In the pre-processing, a colour space conversion, region of interest extraction, and lane marking segmentation are carried out. In the subsequent detection stage, Hough transform is used to detect lanes. Lastly, the lateral offset ratio is computed to yield a lane departure warning based on the detected X -coordinates of the bottom end-points of each lane boundary in the image plane. For lane detection and lane departure detection performance evaluation, real-life datasets for both urban roads and highways in daytime and night-time driving environments, traffic flows, and road surface conditions are considered. The experimental results show that the proposed framework yields satisfactory results. On average, detection rates of 94.71% for lane detection rate and 81.18% for lane departure detection rate were achieved using the proposed frameworks. In addition, benchmark lane marking segmentation methods and Caltech lanes dataset were also considered for comparison evaluation in lane detection. Challenges to lane detection and lane departure detection such as worn lane markings, low illumination, arrow signs, and occluded lane markings are highlighted as the contributors to the false positive rates.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Vision-based lane departure warning framework

Ping

Hossen

Imaduddin

et al. 2019

Heliyon

View full text Add to dashboard Cite

show abstract

“…Researchers exploited the use of these features to detect and recognize traffic signs. Gupta and Choudhary [2] proposed a traffic sign detection and recognition framework based on Grassmann Manifolds. It uses HSV color segmentation for detection.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the figure also shows the fitted cost function on to the bounding box distribution. The cost function can be explained with the following Equations ( 1) and (2).…”

Section: Regressive Anchor Box Selectionmentioning

confidence: 99%

“…Vast research material pertaining to this field is available and researchers are able to achieve high precision and recall values. For example, Serna [1] and Gupta [2] reported near 100% traffic sign detection accuracy on the German Traffic Sign Detection Benchmark (GTSDB) [3]. However, its real-time recognition with resource-limited constraints in a real scenario is still a challenging task.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Small Size Traffic Signs Using Regressive Anchor Box Selection and DBL Layer Tweaking in YOLOv3

et al. 2021

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Traffic sign recognition is a key module of autonomous cars and driver assistance systems. Traffic sign detection accuracy and inference time are the two most important parameters. Current methods for traffic sign recognition are very accurate; however, they do not meet the requirement for real-time detection. While some are fast enough for real-time traffic sign detection, they fall short in accuracy. This paper proposes an accuracy improvement in the YOLOv3 network, which is a very fast detection framework. The proposed method contributes to the accurate detection of a small-sized traffic sign in terms of image size and helps to reduce false positives and miss rates. In addition, we propose an anchor frame selection algorithm that helps in achieving the optimal size and scale of the anchor frame. Therefore, the proposed method supports the detection of a small traffic sign with real-time detection. This ultimately helps to achieve an optimal balance between accuracy and inference time. The proposed network is evaluated on two publicly available datasets, namely the German Traffic Sign Detection Benchmark (GTSDB) and the Swedish Traffic Sign dataset (STS), and its performance showed that the proposed approach achieves a decent balance between mAP and inference time.

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An Overview of Real-Time Traffic Sign Detection and Classification

Taki

Zemmouri

2021

Lecture Notes in Networks and Systems

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A Framework for Real-time Traffic Sign Detection and Recognition using Grassmann Manifolds

Cited by 8 publications

References 18 publications

Vision-based lane departure warning framework

Vision-based lane departure warning framework

Detection of Small Size Traffic Signs Using Regressive Anchor Box Selection and DBL Layer Tweaking in YOLOv3

An Overview of Real-Time Traffic Sign Detection and Classification

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