Real-Time Speed-Limit Sign Detection and Recognition Using Spatial Pyramid Feature and Boosted Random Forest

Gim, Jawon; Hwang, Min-Cheol; Ko, Byoung Chul; Nam, Jae-Yeal

doi:10.1007/978-3-319-20801-5_48

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Zheng et al [18] presented a sliding window detection method, which searches for traffic signs on different scales with the integrated channel feature classifier. Targeting the prohibition and mandatory signs in German Traffic Sign Benchmarks (GTSDB), Gim et al [19] developed a system containing two coarse filter modules: the first module is based on HOG and linear discrete analysis (LDA); the second is based on a small sliding window; both modules involve a large window and an SVM classifier. While these efforts are effective in recognizing traffic signs based on graphical methods, these solutions do not work well in complex scenarios (e.g., low light, signs partially obscured, etc.…”

Section: A Shape-based Traffic Sign Recognitionmentioning

confidence: 99%

Automatic Recognition of Traffic Signs Based on Visual Inspection

Chen

Zhang

et al. 2021

IEEE Access

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The automatic recognition of traffic signs is essential to autonomous driving, assisted driving, and driving safety. Currently, convolutional neural network (CNN) is the most popular deep learning algorithm in traffic sign recognition. However, the CNN cannot capture the poses, perspectives, and directions of the image, nor accurately recognize traffic signs from different perspectives. To solve the problem, the authors presented an automatic recognition algorithm for traffic signs based on visual inspection. For the accuracy of visual inspection, a region of interest (ROI) extraction method was designed through content analysis and key information recognition. Besides, a Histogram of Oriented Gradients (HOG) method was developed for image detection to prevent projection distortion. Furthermore, a traffic sign recognition learning architecture was created based on CapsNet, which relies on neurons to represent target parameters like dynamic routing, path pose and direction, and effectively capture the traffic sign information from different angles or directions. Finally, our model was compared with several baseline methods through experiments on LISA (Laboratory for Intelligent and Safe Automobiles) traffic sign dataset. The model performance was measured by mean average precision (MAP), time, memory, floating point operations per second (FLOPS), and parameter number. The results show that our model consumed shorter time yet better recognition performance than baseline methods, including CNN, support vector machine (SVM), and regionbased fully convolutional network (R-FCN) ResNet 101.

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Section: A Shape-based Traffic Sign Recognitionmentioning

confidence: 99%

Automatic Recognition of Traffic Signs Based on Visual Inspection

Chen

Zhang

et al. 2021

IEEE Access

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“…Although CNN-based method showed a higher classification rate based on the German Traffic Sign Recognition Benchmark (GTSRB) [10], a detailed algorithm for detecting traffic signs on a real road and incorporating the detection into the classifier in real time was not proposed [6]. Unlike CNN-based methods, Gim et al [6] used a two-class boosted random forest with low-dimensional oriented center symmetric-local binary patterns by changing original local binary patterns (LBP). Multi-scale block local binary patterns (MB-LBP) [7] compares the sum of the values of the center blocks with as shown in Fig.…”

Section: ⅰ Introductionmentioning

confidence: 99%

Real-time Traffic Sign Recognition using Rotation-invariant Fast Binary Patterns

Hwang¹,

Ko²,

Nam³

2016

Journal of Broadcast Engineering

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In this paper, we focus on recognition of speed-limit signs among a few types of traffic signs because speed-limit sign is closely related to safe driving of drivers. Although histogram of oriented gradient (HOG) and local binary patterns (LBP) are representative features for object recognition, these features have a weakness with respect to rotation, in that it does not consider the rotation of the target object when generating patterns. Therefore, this paper propose the fast rotation-invariant binary patterns (FRIBP) algorithm to generate a binary pattern that is robust against rotation. The proposed FRIBP algorithm deletes an unused layer of the histogram, and eliminates the shift and comparison operations in order to quickly extract the desired feature. The proposed FRIBP algorithm is successfully applied to German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the results show that the recognition capabilities of the proposed method are similar to those of other methods. Moreover, its recognition speed is considerably enhanced than related works as approximately 0.47second for 12,630 test data.

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