Proceedings of 2011 International Conference on System Science and Engineering, Macau, China - June 2011
Road Sign Recognition System Based on GentleBoost with Sharing Features Jin-Yi Wu, Chien-Chung Tseng, Chun-Hao Chang and Jenn-Jier James Lien*, Member, IEEE Computer Science and Information Engineering National Cheng Kung University Tainan, Taiwan
[email protected]
Ju Chin Chen
Ching Ting Tu
Computer Science and Information Engineering National Kaohsiung University of Applied Sciences Kaohsiung, Taiwan
[email protected]
Institute of Information Science Academia Sinica Taipei, Taiwan
[email protected]
which the previous locating (e.g., detection) is required. Typically, the detection step utilizes the features that are specific to the color and shape of the road sign. The recognition step then uses the road sign textures to indicate what meaning of road sign it is.
Abstract²Nowadays, the number of vehicles is growing rapidly, and more and more intelligent transportation systems are developed for assisting drivers. Road sign detection and recognition is extremely important for safe and careful driving, this system can not only inform the driver about the condition of the roadway but also support the driver during the tedious task of remembering the large number of road signs. In this work, we propose a fast road sign detection and recognition system. This system takes advantage of the HSI color space to filter most of the false alarms. Distance to borders (DtBs) and Support Vector Machine (SVM) are then used for the shape detection. Finally, the candidate blobs that pass through the shape detection is recognized by a GentleBoost with sharing features detector and rotation, scale, translation-invariant (RST-invariant) template matching. For recognition step, color information is used for training GentleBoost detector to ensure the accuracy of the system; and the achromatic part of the candidates are matched to the templates by RST-invariant template matching. The main advantage of this system is that it can detect and recognize road signs efficiently and accurately.
Since road signs must be conspicuous and easily visible to the naked eyes, for most of the conditions the color of the road signs is distinguishable from the background. In the works of [4]-[7], the color information is used to reduce false alarms in the detection process. The work in [8] detects the road signs by optimizing an energy function after a color enhancement stage, and recognizes them by normalized correlation. In [4], hue and saturation are used for reducing false alarms in the detection process; the results are then applied to Support Vector Machine (SVM) for further shape classification and texture recognition. Some methods detect and recognize road signs based on gray-scale images. In recent years, many famous Boosting algorithms have been proposed [9], [10], and are widely used in object detection [11]-[15] with gray-scale images. Authors in [16] proposed a road sign recognition system using evolutionary AdaBoost detector and forest Error-Correcting Output Code classifier to deal with the object detection and recognition, respectively. But these Boosting algorithms usually have to search for every position and every scale in the whole image, and this kind of exhaustive search will be a heavy burden for the system.
Keywords²Traffic sign detection and recognition, GentleBoost with sharing feature, RST-invariant template matching
I.
INTRODUCTION
Road sign detection and recognition has played an important part in the driving assistant system [1]-[3]. Detection and recognition of the road signs allows drivers to be notified about the roadway condition. It can be related to two main goals. First, guide the driver to drive in the correct lane and at the right speed. Second, support the driver during the tedious task of remembering the large number of road signs. In fact, the second goal is much more important than we thought, as sometimes accidents occur due to the misunderstanding of the road signs. Hundreds of roads signs have been invented, but most of the drivers are familiar with only a tiny part of it; the driving assistant system can then provide some guidance such as indicating the meaning of each road sign.
Shape is another powerful visual feature for detection of road signs; each shape has a special meaning which corresponds with some specific colors, such as red triangle stands for danger while red circle means prohibition, and so on. Many robust shape detectors such as the Hough transform [17] are slow, especially when processing a large image. Therefore some other works have implemented a fast algorithm based on radial symmetry which operates on the gradient of a gray-scale image [18], [19]. In this work, we propose a road sign recognition system, which has been successfully applied to Taiwanese road signs. The system contains two modules: 1) detection module and 2) recognition module. First, in the detection module we
Generally speaking, two steps are usually used to solve the problem of road sign recognition: 1) detection and 2) recognition. The road sign recognition system is a straightforward application for object recognition algorithms,
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Input Image
Color-Based Shape-Based Segmentation Using Classification Using HSI Color Space DtBs Features
Detection Module
(a) (b) (c) Fig. 2. Morphology process. (a) Original image. (b) Binary image after red color segmentation. (c) Result of (b) after candidate selection.
Achromatic Part
Recognition Result
RST-Invariant Template
Chromatic Part GentleBoost with Sharing Features h5 H3 h1 h 4 H4 h3 h2 h7 h8 h6 H2 H1
(a) (b) (c) Fig. 3. Part of the shape database for different shapes. (a) Circular. (b) Triangular. (c) Rectangular. All images are collected nearly without rotations, except for circular ones.
Recognition Module Fig. 1. System flowchart for the proposed road sign recognition system consisting of two major modules: Detection and Recognition Module.
detectors, and the achromatic part is applied to the RSTinvariant template matching. RST-invariant template matching stages uses result of GentleBoost detectors to make the final matching result.
discriminate one or more sign candidates, called candidate blobs, from the background in HSI color space by defining a set of color region. And then the candidate blobs are applied to a shape classification detector which is based on distance to borders (DtBs) features, which is also used in [4]. Second, the recognition module takes advantage of the color information of the sign and is combined with texture information. Therefore, the recognition step contains two stages: GentleBoost detectors with sharing features and RST-invariant template matching. Notice that the chromatic part of the sign is helpful for increasing the accuracy of the system; many pre-known information can help us to do some simple clustering before training detectors. Therefore, in the training process of the GentleBoost, chromatic detectors are trained by using chromatic parts of the road signs. Here the detectors are trained using the GentleBoost algorithm with the concept of sharing features [20], [21]. These detectors are then used for test process; each input blob in the recognition step is segmented into two parts, the chromatic part and the achromatic part, which are applied to the GentleBoost detector and RSTinvariant template matching separately. Finally, a fast shape detection method is proposed and GentleBoost with sharing features and RST-invariant template matching are applied to the recognition of road signs; consequently a real-time road sign recognition system that is invariant to translation, rotation, scale, and even partial occlusions is developed. II.
A. Detection Module In the detection module, the discussion focuses on detecting road sign candidates. The detection step contains two stages: color-based segmentation using HSI color space and shapebased classification using HOG features; the prior uses domain knowledge for separating the candidate region out of the background and the latter is done by analyzing the shape information of road signs. Here the inspiration of using color information is due to the reason that traffic, road, or vertical signs are designed using FRORUVWRUHIOHFWVLJQ¶VPHVVDJHDQGWKHVHFKRVHQFRORUVVWDQG out from the environment. Lighting variations and deterioration of the sign are known to be problems for color segmentation. HSI color space is chosen because it gives different pieces of information in every component, and the works in [4], [5] and [8] have proved that the hue and saturation component in the HSI domain are sufficient to isolate road signs in a scene. One other important thing which is needed to be taken into consideration is that the response to varying wavelength and intensity of standard imaging is nonlinear and interdependent [22]. For this reason, to get well-suited thresholds used for color filtering, the database GRAM and image collected by us are used to train the suitable threshold.
TRAFFIC-SIGN-RECOGNITION SYSTEM
$QGWKHQWKHJRDOEHFRPHVWRSUHVHUYHWKH³URDGVLJQ-to-EH´ candidate while eliminating most of the noise. This is done by blob labeling and candidate selection. Each connected object is called a blob, and the candidate selection process will then discard some of them according to its size and aspect ratio. The size of the candidate region is set to be larger than 30x30 pixels which is our template (used in recognition module) size, and the aspect ratio is delimited between 1.9 and 1/1.9 as suggested in [4]. Fig. 2 shows the result of the morphology process. The image patches (blobs) segmented in this step could be in any size and in any aspect ratio, so the geometric normalization is needed for the further step, shape classification step. Fig. 3
The proposed road sign recognition system consists of two modules as shown in Fig. 1. First, in the detection module, HSI color space is used for segmenting the chromatic information, which is then applied to the morphology process. The results (blobs) are passed to the shape-EDVHG FODVVLILFDWLRQ ³DtBs )HDWXUH´LVWKH IHDWXUHXVHGKHUHIRUVKDSHGHWHFWLRQZKLFKLV suitable for the shapes of road signs (ex. circular, triangular, rectangular). When a region has been detected to be in a specific color and the corresponding shape, the region would be recognized in the recognition module. In the recognition module, the regions are separated as chromatic and achromatic parts. The chromatic part is delivered to the GentleBoost
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(a)
(b)
Fig. 4. DtBs for a triangular shape. Traditional multi-class H1: (h1, h10, h22, h45) Not Class 1 Class 1
h1
H2: (h10, h12, h16, h22) Not Class 2
Class 2
(c) (d) Fig. 6. Road sign database, which is clustered into four groups. (a) 48 red triangular signs. (b) 36 red circular signs (including one octagonal sign). (c) 15 blue circular signs. (d) 9 blue rectangular signs.
Sharing features
H3: (h8, h16, h22, h34)
h45
H1
h10 h22 h16
h12
H3
h34 h8
H2 (a)
Class 3 Background Fig. 5. Standard multi-class detector vs. sharing features, where H stands for the strong classifier and h is the weak classifier. h10, h16, h22 are the weak classifiers shared among different strong classifiers.
shows some geometric normalized examples for each shape in RXU GDWDEDVHV WKHVH ³VKDSH GDWDEDVHV´ DUH XVHG IRU DQDO\]LQJ the thresholds, which will be described later.
(b)
(d) (c) Fig. 7. Chromatic parts of the red triangular and circular road signs. (a) Chromatic parts of red triangular signs. (b), (c) Chromatic parts of red circular signs ± type 1 an 2, respectively. (d) Chromatic part of red circular signs ± types 3, 4, and 5 (1 for each).
Then DtBs feature and linear SVM are used to classify the shape of the blobs as [4]. DtBs is the distance to borders as show in Fig. 4. And these distances are used as the features of linear SVM. By using the linear SVM, we can classify the blobs into a certain shape, i.e. circular, triangular, rectangular shapes. For more detailed information, readers can study [4]. Then the classification result is passed to the recognition module. B. Recognition Module Because the false alarm of previous step is pretty high, the loading of recognition step is high. Therefore, the proposed recognition module contains two stages: first, recognition by GentleBoost detector. This step shirks the range of road sign type. And second, RST-invariant template matching is used to recognize the road sign. GentleBoost with sharing features is used for training the Boosting detector. And the RST-invariant template matching adjusts its thresholds depending on the result of the Boosting detector. The advantages of using sharing features are reducing the computing time in weak classifiers. Moreover, traditional multi-class detectors use tree structures which will suffer from the risk of misclassification in the previous node. By the idea of sharing features, the features shared among classes are found, all weak classifiers are applied to the input image at one time, and then the strong classifiers pick the weak classifiers which belong to them. Therefore, the computing time in weak classifiers are reduced and misclassification due to the tree structure is solved. The results of the Boosting detectors are passed to the RST-invariant template matching, which consists of a circular sampling filter, a radial sampling filter and the final template matching filter. The achromatic part of the input image is used here for template matching. The final decision is made by combining the results of the GentleBoost detectors and the RST-invariant template matching.
stronger one. AdaBoost is one of the most famous Boosting algorithms, and has been shown to be robust in many different situations. The idea of sharing features was proposed in [9], and is combined with GentleBoost, an algorithm similar to AdaBoost but with faster convergence and higher accuracy. The main difference between traditional multi-class detectors and sharing features is shown in Fig. 5, the tree structure and the number of weak classifiers. In the standard way, each node separates only one class from the test set, and if a misclassification happens in the previous node there is no chance for a correction. For sharing features, all weak classifiers are applied to the test set at one time. Each strong classifier then picks the weak classifiers which belong to it (the same weak classifier may be picked more than once). Therefore, the tree structure problem that happens in the standard multiclass detector will not appear in GentleBoost with sharing features. The second difference of the sharing features is that the features used (e.g. h10, h16, and h22 in Fig. 5) are shared among different strong classifiers. In this way, it only needs to execute these three weak classifiers once, as opposed to the standard way which may execute these weak classifiers several times. Fig. 6 shows the road sign database (red, blue) downloaded from the government website, with road signs clustered into four groups according to their color and shape. There are 108 road signs, including 48 red triangular signs, 36 red circular signs (including one octagonal sign), 15 blue circular signs, and 9 blue rectangular signs. The road signs are then separated into chromatic and achromatic parts. The former are used in the GentleBoost algorithm and the latter are applied to RSTinvariant template matching. The size of road signs in the database is 30x30-pixel, and the middle part (20x20-pixel) for the red sign is used in recognition step, while the blue sign still uses the whole 30x30-pixel for recognition step. Fig. 7 illustrates the chromatic parts of red signs used in the GentleBoost algorithm; notice that these chromatic parts are binarized by HSI color space. Chromatic parts of the road signs are used for increasing the accuracy of the system. For red circular signs, as illustrated in Fig. 7-(b)(c)(d), it can be preclustered into 5 types; These chromatic parts are used for ensuring the existence of the road signs as well; once the
1) GentleBoost with Sharing Features: Boosting is a well-known classification (and detection) method, which integrates many weak classifiers to form a
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chromatic part from the candidate blob. The template matching in red signs is done by using only the middle part (20x20-pixel) of the road sign. For red circular signs, we categorize the achromatic part into 5 groups (types) according to the chromatic part shown in Fig. 7. RST-invariant template matching proposed by Hae Yong Kim et al. [23]-[26] consists of three steps; a circular sampling filter step, a radial sampling filter step, and a template matching filter step. These three steps work as cascaded filters, and the candidate blob is sent to the next stage only when it passes the current one as shown in Fig. 8.
(a) (b) Fig. 8. Achromatic parts of red circular signs. (a) Achromatic part of Fig. 7.(b). (b) Achromatic part of Fig. 7.(c) (a) Stage 1
(b) Stage 2
Candidate Blob Fail
(c) Stage 3 Pass
Pass
Fail
Recognition Result Fail
Discard
For the red road signs, the result from the GentleBoost detector is used for adjusting the thresholds in this step, for example, once a red circular candidate blob passed the GentleBoost detector with a highest confidence value in type 2, the NCC value thresholds of type 2 templates will be set lower than other types. For the blue road signs, we simply match the complete candidate blob (30x30-pixel) to the templates without isolating the achromatic part. And the recognition result is the road sign with the highest NCC value.
Fig. 9. RST-invariant template matching. Contains three steps: (a) circular sampling filter, (b) radial sampling filter, and (c) template matching filter.
chromatic part of the candidate blob matches one of the types, we lower the threshold for the according type in RST-Invariant template matching due to the high probability that road sign in the same type may appear. Since these 5 types of the chromatic parts in red circular signs have a lot in common, the idea of sharing features is suitable in our case. By using GentleBoost with sharing features [20], a set of strong classifiers can be trained. In these strong classifiers, the common part in different classes will be chosen as a weak classifier feature, which will be shared among different strong classifiers, regard that the specific features will still be chosen for classifying different classes (types).
In the first step, the circular sampling filter (Cifi) uses the descriptors of the candidate blobs and templates on a set of rings (Fig. 9.(a)) to assure the recognition of a rotated sign. The descriptor is build by averaging the grayscale I of the pixels of the candidate blob (and template) situated at a distance r from pixel (x, y). The circular sampling C(x, y)={C(x, y, r), r = 1 to R}, where R is the radius of the template. And C(x, y, r) is define as:
After the training process, five different strong classifiers for the red circular signs are created, one for each type. In testing process, all weak classifiers are applied to the candidate blob at the same time, and each strong classifier then picks the weak classifiers which belong to it and outputs the confidence value of its corresponding type. The candidate blob is categorized to the type with the highest confidence value or to background if the highest confidence value still too low.
C ( x, y , r )
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(1)
Cifi uses the descriptors described above to compute the circular sampling correlation between candidate blob A and all templates with the same shape Ti for the best match:
For red triangular signs, despite the fact that no such preclustering can be made, the chromatic part can still work as insurance for the appearing of the road sign. The candidate blobs and the results of the GentleBoost detector are then delivered to the next stage, RST-invariant template matching.
(2)
Corr (CA ( x, y ), CTi ( x, y )), i
2) RST-Invariant Template Matching: The recognition is based on rotation, scale, translationinvariant (RST-invariant) template matching, combined with the result of the GentleBoost detector. Note that only the ³DFKURPDWLFSDUW´RIWKHURDGVLJQLVPDWFKLQJWRWKHWHPSODWH in the red color road signs. One important reason for doing so is that the outer color (red) regions of the road signs are almost the same when signs are of the same shape and color. In this case, the value of the normalized cross-correlation will be affected greatly by the outer region even though those regions DUHQ¶WRIDQ\XVHLQUHFRJQL]LQJWKHURDGVLJQV$QGVRPHWLPHV the deterioration of the road sign also affects the estimation, such as decreasing the normalized cross-correlation (NCC) value when color fading appears. The achromatic part of the road sign, shown in Fig. 8, is obtained by removing the
where Corr is the normalized cross-correlation between two image patches. If the Corr value is larger than a threshold tc, the template Ti is passed to second step, otherwise, Ti will be discard. The filter used in the second step is called radial sampling filter (Rafi), which uses the descriptors of the candidate blobs and templates on a set of radial lines (Fig. 9.(b)). R(x,y)= R(x,y,Į), Į= 0 ~ 360}, and R(x,y,Į) is the average grayscale of WKH SL[HOV ORFDWHG RQ WKH UDGLDO OLQH ZLWK LQFOLQDWLRQ Į DQG length l: R ( x, y , D )
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The normalized cross-correlation of radial sampling between A and the template Tk, which passed the previous Cifi stage, is done by:
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(a) (b) (c) Fig. 10. Detection result. Top row is GRAM database, and bottom row is our database. (a) Rotation. (b) Different scales. (c) Partial occlusion. (b) Fig. 12. Matrix that relates features to classifiers, which illustrates how the features (white entries) are shared among the different class. (a) Chromatic part of the sign. (b) Achromatic part of the sign. Here we show the first 18 features chosen by the algorithm. TABLE I RECOGNITION RATE AND FALSE ALARMS FOR THE RECOGNITION SYSTEM
Fig. 11. Detection result (failed).
Corr ( RA ( x, y ), cshift j ª¬ RTk ( x, y ) º¼), k
(4) The Proposed System The Proposed System without Using the Chromatic Part
ZKHUH ³FVKLIWj´ PHDQV FLUFXODU VKLIWLQJ j positions of the argument vector. If the highest Corr value with circular shifting j positions is larger than a threshold tr, the template Tk will become Tk' by rotating with the corresponding angle and Tk' will be passed to the final step. III.
Recognition Rate 88.4%
False Alarms 4
83.6%
15
detectors along with the RST-invariant template matching as our recognition module. And the recognition result is shown in Fig. 13, with tc=0.9, tr=0.9, and tm=0.8 for thresholds used in the three steps of the template matching, and tc=0.5, tr=0.5, and tm=0.45 for the corresponding type of the candidate blob. The recognition rate and false alarms over 632 images in our database are shown in Table I, and is compared to the system which uses complete road sign for template matching, i.e., matching without isolating the chromatic part and achromatic part. Fig. 14 shows some failed recognitions: Fig. 14.(a) failed because of the non-standard road signs; the misclassification in Fig. 14.(b) is due to the occlusion (by leaves); and the blurred image and the incorrect crop size result in a missed recognition in Fig. 14.(c).
EXPERIMENTAL RESULTS
The detection rate and the false alarm rate for road signs in GRAM database, which is also used in [27] and [28], is 80.4% and 45.4, respectively. The miss of the detection is because of the blurred image and the reflectance in GRAM database, blurred image will influence the HSI color filtering tremendously, and the reflectance sometimes changes the hue and saturation values in HSI color space, which will also affect the segmentation process. Figs. 10 shows the detection results in different conditions for GRAM database and our database. The detected red signs are surrounded by the blue blocks and the detected blue ones are surrounded by the red blocks for clear view, the left (or right) top of the image shows the color and the shape of the detected signs. And Fig. 11 shows the failed detection and false alarms, most of the failed detections are cause by the incorrect color segmentation or the large percentage in the occluded part, and the false alarms appeared due to the background which has similar shape and color with the road signs.
IV.
CONCLUSIONS
In this work, two modules have been developed for detection and recognition separately; the first detection module is based on a shape classification method with the border HOG features, which is invariant to slightly rotations, and partial occlusions, and the second recognition module coordinates the output of the GentleBoost detector with the output of the RSTinvariant template matching to increase the recognition rate.
For the recognition module, Fig. 12(a) shows the features selected by the GentleBoost algorithm and the sharing matrix that specifies how the different features are shared across 5 types of the circular signs. Each column corresponds to one feature and each row shows the features used for each type. A white entry in cell (i, j) means that type i uses feature j. As we may see, many features are utilized in more than two strong classifiers; therefore, the total computing time in weak classifiers is reduced in this way. We have also test the GentleBoost algorithm on the achromatic part of the sign, the result (Fig. 12.(b)) shows that only few features are shared among different classes, which stands for that the GentleBoost with sharing features is not suitable for distinguish the achromatic part of the sign. Therefore, we use GentleBoost
The experimental result shows that our work is able to accurately classify different shapes of road signs in difficult conditions, for example, rotations, scaling, translations, and even partial occlusions. In addition, the entire road sign recognition system, which did not use special programming skill such as assembly language, can run in almost real-time with 720x480-pixel image (with average 12 fps on a 3.0-GHz CPU). However, some improvements can be done in the future. For image sequences, same false alarm usually will not appear in adjacent frames, this characteristic can be used to reduce the number of false alarms or using different feature rather than
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(b) Fig. 14. Recognition results (failed).
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