A novel traffic sign detection method via color

Neurocomputing 169 (2015) 77–88

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Neurocomputing journal homepage: www.elsevier.com/locate/neucom

A novel traffic sign detection method via color segmentation and robust shape matching Haojie Li a, Fuming Sun b,n, Lijuan Liu a, Ling Wang a a b

Dalian University of Technology, Dalian, Liaoning 116620, China Liaoning University of Technology, Jinzhou, Liaoning 121001, China

art ic l e i nf o

a b s t r a c t

Article history: Received 30 April 2014 Received in revised form 20 October 2014 Accepted 15 December 2014 Available online 28 May 2015

The robust and accurate detection of traffic signs is a challenging problem due to the many issues that are often encountered in real traffic video capturing such as the various weather conditions, shadows and partial occlusion. To address such adverse factors, in this paper, we propose a new traffic sign detection method by integrating color invariants based image segmentation and pyramid histogram of oriented gradients (PHOG) features based shape matching. Given the target image, we first extract its color invariants in Gaussian color model, and then segment the image into different regions to get the candidate regions of interests (ROIs) by clustering on the color invariants. Next, PHOG is adopted to represent the shape features of ROIs and support vector machine is used to identify the traffic signs. The traditional PHOG is sensitive to the cluttered background of traffic sign when extracting the object contour. To boost the discriminative power of PHOG, we propose introducing Chromatic-edge to enhance object contour while suppress the noises. Extensive experiments demonstrate that our method can robustly detect traffic signs under varying weather, shadow, occlusion and complex background conditions. & 2015 Elsevier B.V. All rights reserved.

Keywords: Color invariants Pyramid histogram of oriented gradients Intelligent transportation

1. Introduction In recent years, car ownership is increasing in number with the development of society and economy. There are a lot of people lose their lives due to traffic accidents. How to make the driving safe and convenient has become more and more crucial around the word and Driver Assistance System (DAS) is attracting increasing attentions from computer vision and intelligent transportation research communities. The automatic detection of traffic signs is a key component of DAS. Traffic signs carry much useful information such as speed limit, no tooting, no overtaking, slippery ahead, slow down, etc. Drivers may neglect these vital warning or guide information brought by traffic signs due to fatigue driving, or traffic sign being no-obviously placed. Thus it is demanding for techniques that can automatically detect traffic sign to improve driving safety by alarming dangerous cases. However, traffic sign detection in real scenarios is a challenging task due to the many factors which could degrade the detection system performance, e.g., the cluttered background, various weather conditions, shadows and partial occlusion. To overcome such challenges, features which are invariant to varying imaging conditions and the matching method which is robust to clutters and occlusions are highly desirable. In this paper, we propose a new traffic sign detection n

Corresponding author. E-mail addresses: [email protected] (H. Li), [email protected] (F. Sun).

http://dx.doi.org/10.1016/j.neucom.2014.12.111 0925-2312/& 2015 Elsevier B.V. All rights reserved.

method based on color invariants and pyramid histogram of oriented gradients features. First, we segment the image into different regions to get the candidate regions of interest by clustering the color invariants features, and then PHOG feature is adopted to represent the shape features of ROIs and support vector machine (SVM) is used to identify the traffic signs. The outline of the proposed traffic sign detection system is illustrated in Fig. 1. The original PHOG is sensitive to the cluttered background of traffic sign in practical scenarios. Thus, to boost the descriptive power of PHOG, we propose introducing Chromatic-edge to enhance object contour while suppress the noises. Extensive experiments demonstrate that our method can robustly detect traffic signs under varying weather, shadow, occlusion and complex background conditions, and has achieved higher recall and lower false positive rate as compared to existing algorithms. This paper is organized as follows. Section 2 reviews previous works on traffic sign detection. Sections 3 and 4 describe the two components of our approach in detail. The experiments conducted to evaluate our approach are shown in Section 5. The paper ends with the conclusions in Section 6.

2. Related work Traffic sign detection has been gaining intensive research interests in the past two decades, and many approaches have

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H. Li et al. / Neurocomputing 169 (2015) 77–88

Fig. 1. The outline of our method.

Fig. 2. Typical traffic signs. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

been proposed. Generally, these approaches are mainly based on color and shape features of traffic signs. To make them easily noticeable, traffic signs are usually designed with high-contrastive color such as red, yellow or blue (see Fig. 2). Many approaches focus on using these special colors to segment traffic signs from the background. Zadeh etc [1] built a look-up table sized 256n256n256 in RGB color space and segment the traffic sign by searching the particular color in the color table. Lopez and Fuentes [2] detected the road signs in CIELab color space by modeling color pixels with Gaussian model in order to reduce the illumination influence. L.W.Tsal et al. [3] proposed a novel color model called Eigen-Color which made road sign color more compact to detect road signs color pixels. Color based methods has a lots of challenges in constructing reliable traffic sign detection systems because color is sensitive to various factors like weather, illumination, shadows, color degradation and so on. Traffic signs also have regular shapes such as circle, triangle or diamond (see Fig. 2). There are a lot of methods exploited the invariance and symmetry of the shapes of traffic signs. For example, Hough transform was applied to detect speed limit sign in [4]. To reduce the huge computational complexity of Hough translation, Barnes and Zelinsky [5] improved the Hough translation and proposed a fast radial symmetry detector to detect circular speed limit sign. In order to achieve better results, more and more researches tend to combine the color based method with the shape based method. In [6], Yang et al. converted the original image to a new image using a pre-selected formula and defined the range for the red color, and then identified triangular borders by using a corner detection algorithm. In [7], the red, yellow and blue color were first distinguished by thresholding on H and S components in HSV

Fig. 3. Finding the K-means cluster centers. (a) Two-dimensional histogram. (b) 8-neighbor search window.

color space, then the distance to border (DtB) feature was extracted in the ROIs (regions of interests) to classify the shape using SVM classifiers. Khan et al. [8] extracted gabor features from the a,b components in CIELab, and used them to divide the image into different regions with K-means clustering method. Finally the DtB and Peri2Area features were used to distinguish the shapes of these regions. Le et al. [9] utilized a block of pixels as the input vector of SVM for color classification; after that Hough transform and contour detection were applied to detect the shapes of circle and triangle. Recently, gradient orientation information has been used for detecting and classifying traffic signs in several works, and has


showed the effectiveness. Parada-Loira et al. [10] defined a new local pattern coined as Local Contour Pattern (LCP) which is very similar to the well-known Local Binary Pattern (LBP) to find local geometrical structures over binary images. Zaklouta et al. [11] presented a method for traffic sign detection based on weighted matching of edge orientation histograms which is computed over class-specific sub-regions of the image. In [12], histogram of Oriented Gradients (HOG) which is initially used for pedestrian detection was adopted to detect triangular warning signs. Alefs et al. [13] concatenated the HOG descriptors calculated on each of the color channels to improve the detection performance. Though expressive results have been achieved by these methods, the gradient orientation features are sensitive to background clutters.

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3. Traffic sign ROIs segmentation 3.1. Color invariants Traffic signs are in outdoor environments, thus its segmentation is heavily influenced by the environment where they are actually located in. In general, the RGB color model is sensitive to environmental factors like varying weather, illumination, and shadows, etc. To reduce the adverse effects of such factors, the extraction of invariant color features that reflect the true color of objects is the key problem. In this paper, we propose using color invariants [14], which is extracted with Gaussian color model, as the color representations to segment the target image. To do that,

Fig. 4. Segmentation results. (a) Original image. (b) Segmented image. (c) Candidate blobs after area-based filtering. (d) Candidate ROIs.

Fig. 5. Segmentation results. (a) Original image. (b) Segmented image. (c) Candidate blobs after area-based filtering. (d) Candidate ROIs.

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we translate the RGB color model to Gaussian color model. The equation of linear translation from RGB to Gaussian is as follows. 2 3 2 3 * 0:06 0:63 E 0:27 + R 6E 7 6 7 0:3 0:04 0:35 4 G 5 ð1Þ 4 λ 5¼ Eλλ

0:34

0:6

0:17

B

Where E,Eλ ,Eλλ denote the intensity, blue–yellow and green–red channel. Geosebroek et al. [14] proved that within the Kubelka– Munk model, C λ ¼ Eλ =E and C λλ ¼ Eλλ =E are invariant to several imaging conditions such as viewing direction, surface orientation, illumination direction and illumination intensity. We use these two invariants as image color representations. 3.2. K-means clustering on the input image We collect the C λ , C λλ of each pixel from the input image and get the color invariant feature set f . Then K-means algorithm is applied to cluster the feature f to segment the image into different regions. The K seeds for the K-means algorithm are automatically determined using the hill-climbing algorithm [15] in the twodimensional histogram of color invariants of the image. The hillclimbing algorithm can be seen as an 8-neighbor search window running across the space of the 2-dimensional histogram to find the largest bin within that window. As shown in Fig. 3, the number

of peaks obtained indicates the value of K, and the values of these bins form the initial seeds. The cluster centers and the value K are obtained by searching local maximum value in a two-dimensional histogram. Since Kmeans algorithm clusters pixels in the feature f , an image can be divided into different regions which are accomplished by employing a connected component algorithm on these pixels of each cluster spatially. 3.3. Area-based filtering Since traffic signs are designed to specific shapes, area-based filtering is adopted as a preprocessing step to remove false regions after image segmentation. Given the segmented blobs, our areabased filtering rules are as follows. 1) The aspect ratio of blob should be between 1.9 and 1/1.9. 2) The dimensions of these blobs are delimited between 1/15 and 1/2 of the smaller dimension of the image. Some segmentation results are shown in Fig. 4. Fig. 4(b) shows the clustering results represented with different colors. Fig. 4(c) is the remained candidate blob after area-based filtering and Fig. 4 (d) is the candidate ROI region.

Fig. 6. The extraction of PHOG feature.


Area-based filtering cannot remove some traffic sign alike noisy blobs (as shown in Fig. 5(c) and (d)). To remove these noisy blobs and further classify them into different traffic sign categories, we propose to extract PHOG features of these regions as shape descriptors and feed them into SVM for shape verification and classification.

Step 1: Canny edge detector is used to get the edge contours, as shown in Fig. 6(b).

Step 2: The edge image is divided into cells at several pyramid

4. Traffic signs detection based on PHOG

4.1. Extraction of PHOG feature Recently, PHOG feature proposed by Anna Bosch [16] has been wildly used in object detection, human facial expression recognition and classification, vehicle classification and image retrieval. As a spatial shape descriptor, it can represent the statistical information of the shape globally and locally, making it effective for object recognition. The details for extracting PHOG features are as follows.

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levels. As shown in Fig. 6(b), the grid at level l has 2l cells along each dimension. Step 3: The HOG for each cell at each pyramid resolution level is computed. Thus local shape feature can be described by the histogram of edge orientations generated within an image subregion, as shown in Fig. 6(c). Step 4: The final PHOG descriptor for an image is a concatenation of all the HOG vectors at all pyramid resolutions.

As shown in Fig. 6, PHOG is quite sensitive to the results of contour detection. If the extracted contour is not clear or has lots of noise, the descriptive power of PHOG will degrade dramatically. Unfortunately, traffic signs are usually sited in natural environments, which make the extracted contour have much noises when using Canny detector. As

Fig. 7. PHOG for a triangular shape traffic sign.

Fig. 8. PHOG for a circular shape traffic sign.

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traffic signs always have eye-catching colors, in this paper, we propose adopting chromatic-edge method [14] to extract contour points while reduce the noise points. The definition and computing formulas of chromatic-edge with color invariants are as following (2)–(4): E E E Ex C λx ¼ λx 2 λ E

ð2Þ

E E E Ex C λλx ¼ λλx 2 λλ E

ð3Þ

where Ex , Eλx , Eλλx are the differentials to x of E, Eλ, Eλλ in (1). C λy , C λλy are yielded by applying the same method to differentials of y. Finally, the total edge strength denoted as C ω is calculated as: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi C ω ¼ C 2λx þ C 2λy þ C 2λλx þ C 2λλy ð4Þ For example in Figs. 7–10, “Canny” denotes the extracted edge points by using the canny algorithm in ROI. “C ω ” denotes the gradient

magnitude of C ω calculated according to Eq. (4) where the brightness indicates edge strength. In general, there is a great difference between the color of the traffic sign's border and the sign's background because traffic signs are designed with high-contrastive color. Therefore, the edge strength of traffic sign's border is strong. Otsu method [17] is applied on the chromatic edge image to filter out weak edges and leave the strong edge points denoted as “C ω T ”. As shown in Figs. 7–10, the noises are dramatically reduced by using the chromatic edge method, as compared with the Canny detector. We obtain the improved PHOG descriptor by concatenating all the HOG vectors at each pyramid resolution. In this paper we set 9 orientation bins in the range of [01, 1801], namely, the interval is 201. The number of layers L is set to 3. Thus a 189 dimensional PHOG features is obtained. Figs. 7– 10 show the PHOG features of each layer for the shape triangle, circle, inverted triangle, and diamond. From these figures we can see that PHOG is discriminative to distinguish the various shapes of traffic signs.

Fig. 9. PHOG for a inverted triangular shape traffic sign.

Fig. 10. PHOG for a diamond shape traffic sign.


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Fig. 11. Examples of traffic sign training (a) and testing (b) samples. From the first row to the fifth row are triangle shape, circular shape, inverted triangle, diamond and common negative samples.

Fig. 12. Fade traffic sign's segmentation. (a) Blue traffic sign, (b) HSI segmentation result, (c) Segmentation of proposed approach, and (d) Candidate blobs after area-based filtering. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 13. Fade traffic sign's segmentation. (a) Red traffic sign, (b) HSI segmentation result, (c) Segmentation of proposed approach, and (d) Candidate blobs after area-based filtering. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 14. Traffic sign segmentation under bad illumination. (a) Traffic sign, (b) HSI segmentation result, (c) Segmentation of proposed approach, and (d) Candidate blobs after area-based filtering.

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4.2. Building traffic sign classifiers using support vector machines SVM is a successful machine learning technique for data classification based on statistical learning theory [18]. In this paper

Table 1 Comparison of different traffic signs detection methods. Traffic signs

Numof signs

Precision (%) Ref. [7]

Triangle Circle Inverted triangle Diamond

310 309 55 62

CannyPHOG

Recall (%) Our

Ref. [7]

CannyPHOG

we use SVM to classify the extracted PHOG features of ROIs into different categories. We build four binary SVM shape classifiers to classify the input PHOG features to be triangle (label '1') or not triangle (label -'1'), circular (label '1') or not circular (label '-1'), triangle (inverted) (label '1') or not triangle (inverted) ('-1'), diamond (label '1') or not diamond (label '-1'). A voting method is applied to determine the classification result of a given PHOG. Thus, if the voting score is not equal to 2, the underlying ROI will be a noise region; otherwise it should be one of the traffic sign shapes.

Our

5. Experimental results

88.70 92.25 84.14 86.95 81.82 90.91

96.45 73.53 92.86 93.52 83.6 87.72 94.50 70.97 86.21

98.04 93.23 96.3

5.1. Dataset

—

95.16 —

89.4

To train the four SVM shape classifiers, we build our dataset which contains 1000 training samples as follows. The triangular, circular,

93.54

74.36

Fig. 15. Traffic signs detection under different kinds of weather. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

Fig. 16. Traffic sign's detection under different illumination conditions. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)


inverted triangular and diamond shape samples come from [19,20], and each shape has 200 samples. A common set of 200 negative samples contains no traffic signs are sampled from street photos in [20]. Some example images are shown in Fig. 11(a). These training samples are scaled to 128n128 by bilinear interpolation. The final training samples contain 200 positive samples and 800 negative samples for each shape category. We also build a testing dataset (see Fig. 11(b)) to evaluate the proposed method in detecting traffic signs by sampling images from [6,7,9]. The resulted dataset consists of 500 images for the four traffic signs, and some of them include partial occlusions, different lightings, or shadows. 5.2. The experimental results In this section, we first conducted experiments to evaluate the effectiveness of the proposed color invariants based image segmentation, and then validated the proposed traffic sign detection method by comparing the Canny-PHOG method and other stateof-the-art method.

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5.2.1. Evaluation on the color invariants based clustering for image segmentation The first stage of the proposed method is to segment the image into candidate ROIs using the color invariants based clustering. We evaluated our segmentation method and compared it with the HSI (Hue–Saturation–Intensity) based segmentation method [7]. The HSI color space is widely used in computer vision applications and is similar to the way humans perceive color and is robust to illumination. We compared the segmentation methods under different conditions and the results showed that the color invariants representation is superior to HSI representation for traffic sign segmentation for most cases. In the following we will give some segmentation examples. Color fading is common for traffic sign. Figs. 12 and 13 show two such examples where the blue and red signs fade due to long time exposure outdoors. We can see that the supporting post was not clearly separated from the signs (see Fig. 12(b)) and the red regions were not completely segmented (see Fig. 13(b)) using the HSI segmentation method. While our approach successfully segmented the traffic signs for these fading cases.

Fig. 17. Traffic sign's detection under shadows. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

Fig. 18. Traffic sign's detection with occlusion. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

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Fig. 19. Fade traffic sign's detection. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

Fig. 20. Traffic sign detection in complexity backgrounds. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

Illumination is another important factor that can affect the segmentation of traffic sign from the background. Fig. 14 shows an example of traffic sign segmentation under lower illumination. In this case, the HSI method failed to separate the sign from the background, while the proposed color invariant based clustering approach had obtained the clear outline of the sign.

5.2.2. Evaluation on the proposed traffic sign detection method We compared our proposed traffic sign detection method with the method in [7] (denoted as Ref. [7], which used HSI segmentation, DtB features and SVM classifiers) and Canny-PHOG shape classifies method (denoted as Canny-PHOG) on the 500 testing

images and the results are tabulated in Table 1 (‘–’ indicates that [7] does not give the method for diamond shape detection). The experimental results showed that our proposed approach achieved higher precision and recall. The results also validated that the proposed chromatic-edge enhanced PHOG is more discriminative than canny-PHOG in distinguishing the various shapes of traffic signs. Some detection results under different conditions are illustrated in Figs. 15–21, where the detection results for triangle, circle, inverted triangle, and diamond traffic signs are labeled using red, blue, green, and yellow rectangles respectively. Figs. 15–21 show the examples of detected traffic signs for different weather conditions, illumination conditions, partial occlusions, shadow affects, fade signs, complicated


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Fig. 21. Multiple traffic sign detection. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

inner area of traffic sign which we have obtained after K-means clustering on color invariants feature to recognize the types of traffic sign.

Acknowledgments This work was partially supported by National Natural Science Funds of China (61272214, 61173104, 61472059). References

Fig. 22. Some wrong detection results.

backgrounds and multiple signs. These results showed that our proposed approach is quite robust to these influences. We showed some wrong detection results for our method in Fig. 22. In the first row, the background regions were recognized as traffic signs because they have evident traffic sign like shapes, e.g., circle and triangle. The second row of Fig. 22 showed some missed traffic signs, which were caused by the blurring and rotation of signs. It is noted that PHOG is sensitive to shape rotation as it is derived from the statistics of oriented edges. The diamond sign was missed due to the occlusion which has changed the PHOG of the sign.

6. Conclusions In this paper we have proposed a novel approach based on color invariants and improved PHOG to detecting traffic signs. The contributions of our work are two-fold. Firstly, the proposed color invariant based clustering is effective in traffic sign segmentation than other widely used segmentation methods such as HSI segmentation. Secondly, we boosted the discriminative power of PHOG by using Chromatic-edge to enhance object contour while suppress the noises. Thus the proposed traffic sign detection approach is quite robust to various factors such as shadow, occlusion, weather, complex background and so on. In the future, we will investigate the use of the

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Dr. Haojie Li is a Professor in the School of Software, Dalian University of Technology. He received the B.E. and the Ph.D. degrees from Nankai University, Tianjin and the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, in 1996 and 2007 respectively. From 2007 to 2009, he was a Research Fellow in the School of Computing, National University of Singapore. He is a member of IEEE and ACM. His research interests include computer vision, social media computing and multimedia information retrieval. He has authored over 30 journal and conference papers in these areas, including IEEE TCSVT, TMM, ACM Multimedia, ACM ICMR, etc.

Fuming Sun is currently a Professor with the School of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou, China. He received the Ph.D. degree from the University of Science and Technology of China, Hefei, China, in 2007. From 2012 to 2013, he was a Visiting Scholar with the Department of Automation, Tsinghua University. His current research interests include content-based image retrieval, image content analysis, and pattern recognition.

Lijuan Liu is a postgraduate student in the School of Software, Dalian University of Technology. He received the B.E. degree from Dalian University of Technology, Dalian, China in 2013. Her research interests include computer vision and multimedia information retrieval.

Ling Wang is a postgraduate student in the School of Software, Dalian University of Technology. He received the B.E. degree from Dalian University of Technology, Dalian, China in 2014. His research interests include computer vision and multimedia information retrieval.