2016 19th International Conference on Computer and Information Technology (ICCIT) 2016
DOI: 10.1109/iccitechn.2016.7860241
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Traffic sign recognition using hybrid features descriptor and artificial neural network classifier

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Cited by 13 publications
(16 citation statements)
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“…But these methodologies are also hampered due to the presence of various drawbacks. The systems presented in [1], [4], [7], [8], [10], [11] and [12] fail in considering a vast variety of both Training and Testing datasets, that can be considered to scale the respective systems. [3] and [6] fail on being tested in harsh weather conditions.…”
Section: Behrendt Et Al Have Proposed a Deep Learning Approach Tomentioning
confidence: 99%
“…But these methodologies are also hampered due to the presence of various drawbacks. The systems presented in [1], [4], [7], [8], [10], [11] and [12] fail in considering a vast variety of both Training and Testing datasets, that can be considered to scale the respective systems. [3] and [6] fail on being tested in harsh weather conditions.…”
Section: Behrendt Et Al Have Proposed a Deep Learning Approach Tomentioning
confidence: 99%
“…Research has been conducted on the recognition of traffic signs on the road [4,5]. Based on images captured by a self-driving car, a traffic sign is recognized using a CNN [5].…”
Section: Research On Awareness Of Carsmentioning
confidence: 99%
“…The traffic sign shape is then identified using the distance-to-borders (DtBs) approach. Zainal et al [4] propose recognizing traffic signs using an artificial neural network (ANN) based on two feature descriptors, the histogram-oriented gradient (HOG), and speeded-up robust features (SURF). The velocity of the car is controlled based on the detected traffic signs.…”
Section: Research On Awareness Of Carsmentioning
confidence: 99%
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“…To examine the significance of our proposed method, one baseline was created from the collected dataset for this research, namely, the histogram of oriented gradients (HOG) technique proposed in [67,68]. In [67] hybrids of HOG and SURF features descriptor was used with ANN classifier to select the most important and discriminative features. In [68] HOG based feature extraction technique with SVM and Random Forest, classifiers were used to recognize traffic sign images.…”
Section: Significance Of Dataset and Proposed Approachmentioning
confidence: 99%