Chapter 32
Traffic Sign Detection and Classification for Driver Assistant System Nursabillilah Mohd Ali, Nur Maisarah Mohd Sobran, M. M. Ghazaly, S. A. Shukor and A. F. Tuani Ibrahim
Abstract In this paper we explain the proposed method of traffic sign detection and classification for driver assistant system (DAS). Color detection framework using RGB method is utilized in this study, whereas an artificial neural network (ANN) has been used as classifiers for classification. There are at least 100 types of Malaysian Traffic Signs have been employed in this research. Most of the images are taken at various places throughout the urban and suburban areas involved with scale, illumination and rotational changes as well as occlusion images. The experimental results are shown that the proposed framework achieved at least 80 % successful detection with 21 false positive images. On the other hand, the ANN gives strong rates where at least most of the signs can be classify with more than 85 % success. Keywords Color detection images
Illumination-invariant Classification of occlusion
32.1 Introduction Of late, Driver Assistance System (DAS) was created and become the most popular topic in computer vision application, i.e. traffic sign detection and recognition. It has contributes to road safety and awareness among drivers for the last 15 years. Since then, many techniques have been created and implemented to achieve accurate and robust performance towards traffic sign identification system [1]. N. M. Ali (&) N. M. M. Sobran M. M. Ghazaly S. A. Shukor A. F. T. Ibrahim Department of Mechatronics, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia e-mail:
[email protected];
[email protected] N. M. M. Sobran e-mail:
[email protected]
H. A. Mat Sakim and M. T. Mustaffa (eds.), The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications, Lecture Notes in Electrical Engineering 291, DOI: 10.1007/978-981-4585-42-2_32, Springer Science+Business Media Singapore 2014
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Even though many techniques have made various algorithms and equipped with good system performance, the disorganized comparisons are still missing and datasets are not easily available. Detection and recognition stages are a multitaskbased category as many datasets are used as a training set to achieve the result. Driver’s vision is easily distracted by the headlight of the oncoming vehicles at night, which make driving more difficult and hence, lead to more traffic accidents. Traffic signs are not only used to regulate congested traffic but also give useful information in order to avoid accidents that might happen. Traffic signs can be used to differentiate their information based on colours and shapes. They are designed so that drivers can easily detect and recognize them. In Malaysia for instance, more than 100 road signs classes can be investigated [2]. The paper is organized as follows: Sect. 32.2 entails the survey on traffic signs. Section 32.3 discusses the system overview about the proposed system and addressed the result. Finally the analysis made before conclusions in Sect. 32.4.
32.2 Literature Survey There are many existing techniques for traffic sign identification that has been used. Traffic sign detection has been treated with simple background images by many existing research. However, complex background is an important aspect as we need to observe the performance result. Color is an important part in detection phase. Several researchers make used of the advantages of color model to build algorithm for traffic sign detection. It is due to several variances that might affect the appearance of the signs. There are many color model that have been used has compromised HSV [3] or HSI [4], YUV [5] and YCbCr. Besides that, several researchers have developed color of databases, look-up tables and hierarchy region growing technique so that the detection technique would be much easier. On the other hand, in traffic sign classification and recognition, many researchers used template matching, Latent Dirichlet Allocation (LDA), Support Vector Machine (SVM), and other learning-based method. Pictogram have been classified using template matching and cross correlation using OCR (optical character recognition) systems. Moutarde et al., employed traffic system for speed limit signs based on single digit recognition using neural network technique. However, the recognition result was not represented by the developed system. The system achieved almost 89 and 90 % for U.S. and European, respectively using 281 traffic signs. Yet, this method still using certain signs such as speed limit and not focusing on the all classes.
32.3 Proposed Framework The paper focused on development of detection using color segmentation and classification using neural network. Figure 32.1 shows the proposed technique. It consists of 3 main steps. Twelve types of traffic signs were used: Uneven Road, No
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Fig. 32.1 Algorithm development process. (I) Image acquisition; (II) color segmentation; (III) detection process; (IV) classification/recognition process
Entry, No-U-Turn, Right Junction, Yield/Give Way, Keep Both Side, Stop Sign, Keep Left, Left Junction, Speed Limit 30 km/h, Speed Limit 50 km/h and Speed Limit 60 km/h signs. These signs were chosen because they were the most common traffic signs. For recognition stage, experiments conducted included partial occluded signs, illumination changes and rotational changes.
32.3.1 RGB Color Segmentation Utilized by Aspect Ratio Determination This research utilized algorithms between using RGB color space. The algorithms were able to detect red, blue and yellow traffic signs, respectively. Algorithms were tested on static images separately. Images were mostly taken during sunny day and at night. Tests were carried out over 403 images which represented 101 partial occluded and 253 non-partial occluded signs with 49 rotational changes signs.
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N. M. Ali et al. No.
Traffic signs
Color channel
1 2 3
Red Blue Yellow
Red/Green Blue/Green Green/Blue
Fig. 32.2 Color segmentation using RGB color ratio with aspect ratio determination, at night (1, 4 and 5) and daytime (2 and 3)
The traffic signs can be affected by the illumination conditions such as direct sunlight, shadows and reflected sunlight which make detection more difficult. In this research, the images used were in resolution of 500 9 667 pixels using digital camera. The objective of the research is to detect any sign of given colour regardless of the illumination changes with partial occlusion.
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Fig. 32.3 Classification for each signs classes for test and all confusion matrixes with their respective percentages
Color segmentation using RGB algorithm was implemented based on Table 32.1. The color ratio in Fig. 32.2 was able to detect traffic signs with red, blue, yellow, white and black colours, respectively. However, achromatic color which is represented by dominant colour of hue is used to search the pixel of interest (POI) within traffic signs. The equation is as the following f ðR; G; BÞ ¼
ðjR Gj þ jG Bj þ jB RjÞ 3D
ð32:1Þ
The aspect ratio utilized in the color detection if the minimum bounding box size is less than 300 pixels and ratio is less than 1.2. The determination was able to detect at specific traffic sign using RGB color segmentation.
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Fig. 32.4 Receiver operating characteristic (ROC) rate
32.3.2 Classification Stage: An Neural Network Approach A supervised feed forward neural network was utilized in this study. It is a recursive tool that really helps in object classification. It can also be regarded as a pattern recognition tool. It extracts patterns when given input based on specific conditions of target. The flow of neural network will basically cover generation of weight and bias, trained, validated and tested. The neural network was trained using scale conjugate gradient (SCG) back propagation algorithm using log sigmoid function with 100 numbers of epochs. Figure 32.3 illustrates the 12 sign classes that are classified according to its class by dividing the network for training, testing and validation. Based on the training data, the network divides 70 % for training, 15 % for testing and 15 % for validation’s confusion matrix. After running the ANN algorithm, for instance, the percentage of confusion matrix of training uneven signs target which is represented at first row at Fig. 32.3 (training confusion matrix) obtained 100 %
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accuracy. However, when the network tried to validate the data, it gives 75 % correctness whereas 66.7 % correctness for testing part. Based on the all confusion matrix data as shown at Fig. 32.3, 93.8 % classifies the first class as uneven signs target whereas 6.3 % produces wrong classification. Based on this classification result, Fig. 32.4 shows the 12 sign classes of ROC value that are respectively reserved for training, testing and validation. ROC is widely used in predicting the percentage of accuracy in pattern recognition technique. As can be seen from the all ROC graph, the all classes approach true positive (TP) rate which is approximately one. TP is number of correctly predicted meaning that less false positive rate occurred in classification the signs classes.
32.4 Conclusion In a nutshell, we have explained an algorithm using RGB color space in traffic sign detection. It was shown that the algorithm is able to detect red, yellow and blue traffic signs respectively using RGB color space. This frame able to detect more than one sign and reduced the false positive rates. Based on the result, the algorithm not only can detect partially occluded sign, but also all colors representing traffic signs involved with illumination and rotational changes. As for classification stage, a supervised neural network is used to classify the sign according to its class. This classifier can be extending for future research such as identification of traffic sign. In future research, we are planning to optimize the detection and classification algorithm to be applied in real time traffic sign classification and recognition application.
References 1. Nguwi YY, Kouzani AZ (2006) A study on automatic recognition of road signs. In: 2006 IEEE conference on cybernetics and intelligent systems, pp 1–6 2. Lim KH, Seng KP, Ang LM (2010) Intra color-shape classification for traffic sign recognition. In: International computer symposium (ICS) 2010, pp 642–647 3. Hua H, Chao C, Yulan J, Shinning T (2008) Automatic detection and recognition of circular road sign. In: IEEE/ASME international conference on mechatronics and embedded systems and applications (MESA) 2008, pp 626–630 4. Pazhoumand-Dar H, Yaghobi M (2010) DTBSVMs: a new approach for road sign recognition. In: Proceedings of the 2nd international conference on computational intelligence, communication systems and networks (CICSyN) 2010, pp 314–319 5. Vitabile S, Pollaccia G, Pilato G, Sorbello F (2001) Road signs recognition using a dynamic pixel aggregation technique in the HSV color space. In: Proceedings of the 11th international conference on image analysis and processing, pp 572–577
