Multiple thresholding and subspace based approach for detection and recognition of traffic sign

Gudigar, Anjan; Shreesha, C; Raghavendra, U.; Acharya, U. Rajendra

doi:10.1007/s11042-016-3321-6

Cited by 30 publications

(16 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…L is a grayscale image with foreground pixels taking higher grayvalues than the background pixels. In order to threshold it to a black and white image, L is subjected to maximally stable extremal region (MSER) technique [2], [36]. Using this method, the input grayscale image is thresholded repeatedly at different threshold values and the regions found to be the most stable at majority of the threshold values are retained.…”

Section: ) Segmentationmentioning

confidence: 99%

“…A recently proposed random forest based technique [4] using HSI thesholding and HOG features performed poorly for degradations present on the outer rim. The last method [36] uses multiple thresholding technique similar to maximally stable extremal region for detection and log polar transforms for recognition. Our proposed method obtained the best detection rate for degradations present on rims.…”

Section: ) Overall Performancementioning

confidence: 99%

See 1 more Smart Citation

Classification of Degraded Traffic Signs Using Flexible Mixture Model and Transfer Learning

et al. 2019

View full text Add to dashboard Cite

Automatic detection and recognition of traffic signs is a topic of research for various applications like driver assistance, inventory management and autonomous driving. Poorly maintained traffic signs degrade by losing their colors or some part is weird due to aging and hence making the task more challenging. The problem is mainly related to the developing world and has gained less attention in the literature on automatic traffic sign detection and recognition. To handle the degradation issue, we present a novel flexible Gaussian mixture model based technique with automatic split and merge strategy. This adaptive scheme works as a preprocessing step which facilitates locating traffic signs under a certain degree of degradation in a real world scenario. A multiscale convolutional neural network augmented with dimensionality reduction layer is proposed to recognize contents of the sign. Since, there is no available benchmark dataset for this purpose, we collected a number of images containing partially degraded signs from the famous German Traffic Sign Detection Benchmark (GTSDB) and augmented it with manually and naturally degraded traffic sign images taken from the longest highway in the country of authors' residence. Experimental results show that our proposed technique outperforms many state of the art and recent methods for detection and recognition of degraded traffic signs. INDEX TERMS Degraded traffic sign, Gaussian mixture model, Transfer learning, Convolutional neural networks, dimensionality reduction.

show abstract

Section: ) Segmentationmentioning

confidence: 99%

Section: ) Overall Performancementioning

confidence: 99%

Classification of Degraded Traffic Signs Using Flexible Mixture Model and Transfer Learning

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Regarding shape, the shape-based candidate detection approaches mainly base on the parameters of the shape such as corner, area and perimeter, Fast Fourier Transform (FFT) signatures of candidate signs, the number of sides of a given shape and hole based approach are used to determine the appropriate shapes [6]. The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for capturing the shape of the object [7].…”

Section: Shape-based Approachesmentioning

confidence: 99%

Circular Traffic Signs Detection in Natural Environments

Huang¹,

Huang²

2017

dtcse

View full text Add to dashboard Cite

This paper describes an efficient method for the detection of circular traffic signs on color images in different natural environments. First, a RGB image was converted into HSV color space which was segmented by the hue and saturation thresholds, so that we can extract the region of interests (ROIs). Due to which, A Circular Hough Transform (CHT) based algorithm is proposed to find circles in the image, after that the traffic signs are detected from the images under natural scenes. Experiments were conducted for the detection of traffic signs which involved two traffic sign image databases. The experimental results indicate that the proposed algorithm possess of high detection accuracy, and good robust performance.

show abstract

“…In color-based methods, RGB images are usually converted into other color spaces, such as HSI [6], CIELab [7], and HSL [8]. Then, the traffic signs are extracted via color threshold segmentation through intelligent data processing [9]. Color-based detection methods are usually vulnerable to complex lighting conditions in the traffic scene.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition

Jin

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Road traffic sign detection and recognition play an important role in advanced driver assistance systems (ADAS) by providing real-time road sign perception information. In this paper, we propose an improved (Single Shot Detector) SSD algorithm via multi-feature fusion and enhancement, named MF-SSD, for traffic sign recognition. First, low-level features are fused into high-level features to improve the detection performance of small targets in the SSD. We then enhance the features in different channels to detect the target by enhancing effective channel features and suppressing invalid channel features. Our algorithm gets good results in domestic real-time traffic signs. The proposed MF-SSD algorithm is evaluated with the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The experimental results show that the MF-SSD algorithm has advantages in detecting small traffic signs. Compared with existing methods, it achieves higher detection accuracy, better efficiency, and better robustness in complex traffic environment.

show abstract

Multiple thresholding and subspace based approach for detection and recognition of traffic sign

Cited by 30 publications

References 47 publications

Classification of Degraded Traffic Signs Using Flexible Mixture Model and Transfer Learning

Classification of Degraded Traffic Signs Using Flexible Mixture Model and Transfer Learning

Circular Traffic Signs Detection in Natural Environments

Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition

Contact Info

Product

Resources

About