Abstract:
The present invention relates to an apparatus and method for detecting and recognizing an object in an image, the method comprising a plurality of stages, wherein at least one of the stages comprises an integrated approach of feature detection and object recognition of at least a part of the object. In a further embodiment at least one of the stages comprises identifying an image part that contains a feature point, and matching the image part to a set of hierarchies of templates, wherein a hierarchy comprises templates for an object to be recognized, a template describes at least a part of an object to be recognized, and a child template describes a sub-part of the part of the object described by its parent template.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This national stage patent application under 35 U.S.C. §371 claims priority to PCT application no. PCT/EP2011/068985 filed Oct. 28, 2011, which claims priority to European Patent Office patent application no. 1018922.5 filed Oct. 28, 2010, the disclosures of each of which are incorporated herein by reference for all purposes. 
     BACKGROUND 
     The disclosure relates to a method for detecting and recognizing an object in an image. 
     The disclosure further relates to an apparatus configured for performing such a method. 
     Furthermore, the present disclosure relates to a computer program that, when executed on a processor, performs such a method. 
     The present disclosure also relates to a data carrier with such a computer program stored thereon. 
     During the last decade a vast amount of digital images has been structurally acquired on public roads. These digital images are being used extensively in, among others, the real estate market, infrastructural planning, and the investigation of local traffic situations by organizations such as governments, municipal authorities, real estate agents and insurance companies. For a major part, these images have been used in a non-automated fashion, wherein a person investigates a set of images and manually retrieves the required information from the images. 
     One interesting application of these images is to determine the location and type of road signs along public roads. This information is used by cartographers, for example to determine vehicle restrictions and speed restrictions of roads. 
     SUMMARY 
     The present disclosure seeks to provide an automated method for detecting and recognizing objects, in particular road signs, in digital images. 
     This object is reached by a method comprising a plurality of stages, wherein at least one of the stages comprises an integrated approach of feature detection and object recognition of at least part of the object. By combining different templates in a database for objects and using those templates to recognize the occurrence of a template at the detection phase, the recognition of that template is facilitated. By integrating the detection and recognition of an object, a higher accuracy is obtained in conjunction with a relatively high processing speed, as false candidates that strongly trigger the detector but score low in the recognition process, are removed from the process at an early stage of the process. 
     The present disclosure further provides a method, wherein at least one of the stages comprises: identifying an image part that contains a feature point; and matching the image part to a set of hierarchies of templates, wherein: a hierarchy comprises templates for an object to be recognized, a template describes at least a part of an object to be recognized, and a child template describes a sub-part of the part of the object described by its parent template. This way the detection and recognition process take place at more than one level in the hierarchy. For each hierarchy, level detection and recognition takes place in an integrated manner. 
     In another aspect of the disclosure, feature points are matched to a template only after they have been successfully matched to at least one of its child templates. 
     In again another aspect of the disclosure, the set of hierarchies of templates comprise a library. 
     In a further aspect of the disclosure, a method is provided, wherein a template in the library comprises an indication of the confidence of the detection/recognition. 
     Preferably, the matching comprises comparing colors in an identified image part to a color description in a template. This is specifically advantageous when detecting and recognizing colorful objects such as traffic signs. 
     In another aspect of the disclosure, the templates define a background mask, wherein the background mask determines areas to be ignored during matching, wherein an area comprises a group of pixels and/or a group of sub-pixels. 
     The present disclosure also provides a method wherein the objects to be detected and recognized comprise traffic signs. 
     In one embodiment of the disclosure, an apparatus is provided configured for performing a method according to the present disclosure. 
     In another embodiment, a computer program is provided, that, when executed on a processor, performs a method in accordance with the present disclosure. 
     In again another embodiment, a data carrier is provided with a computer program in accordance with the disclosure stored thereon. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the present disclosure are illustrated in the following drawings which depict various embodiments according to the disclosure. These drawings are not intended to limit the scope of the disclosure. 
         FIG. 1  illustrates a flow chart in accordance with embodiments of the present disclosure; 
         FIG. 2  illustrates a flow chart of a step from the flow chart of  FIG. 1  in accordance with embodiments of the present disclosure; 
         FIG. 3  illustrates a detail of  FIG. 2  as applied to an image in accordance with embodiments of the present disclosure; 
         FIG. 4  illustrates a step from the flow chart of  FIG. 1  as applied to an image in accordance with embodiments of the present disclosure; 
         FIG. 5  illustrates a further step from the flow chart of  FIG. 1  as applied to an image in accordance with embodiments of the present disclosure; 
         FIG. 6  illustrates templates used in a method according to embodiments of the present disclosure; 
         FIG. 7  illustrates a further flow chart of a step from the flow chart of  FIG. 1  in accordance with embodiments of the present disclosure; 
         FIG. 8  shows a detail of flow chart of a method according to the present disclosure; and 
         FIG. 9  illustrates an exemplary library with templates for use in a method according to embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     One specific method according to the present disclosure, the specific method being suitable for the detection and recognition of traffic signs, comprises three stages ( FIG. 1 ): a processing stage  130  that pre-processes the input image  110 , a processing stage  140 , and a post-processing stage  150 . The result is one or more traffic signs  190  that were detected and recognized in the image, with additional properties of the traffic signs and/or the detection and/or recognition of the traffic signs. 
     The processing stage  130  prepares the input image  110  for the processing stage  140 . The processing stage  130  operates on the whole image  110 . 
     The pre-processing stage  130  comprises several steps, such as: an image acquisition step  131 , a color selection step  132 , an image setting step  133 , a search area step  134 , and an integral image computation step  135 . 
     The first step of the pre-processing stage  130  comprises the acquisition  131  of an image  110 . The image  110  may be acquired straight from an image capture device, but will usually be read from a storage device. This image  110  is converted to the YCbCr color space. An example is the use of JPEG images as input. During the necessary decoding of the JPEG image, the YCbCr values of the pixels become available. 
     In the color selection step  132 , the YCbCr space is used to classify the pixels in color classes relevant for the traffic signs to be detected. In alternative embodiments, other color spaces are used for the classification of pixels. In order to obtain a segmentation result from the input image  110 , all pixels are individually classified depending on the value of their color components by testing the individual components independently. If they fall in a certain color range, or if the color components lead to a specific color combination, a pixel is classified in a respective color class. The resulting image is called the segmented image (with respect to color). 
     To obtain the image to filter, the luminance component is taken, adapted with the color components of the YCbCr color space. By subtracting the red difference color component from the blue difference color component, a larger gap between the different colors is generated. This difference is added to the luminance:
 
 p ( x,y )= Y ( x,y )+ a·C   b ( x,y )− b·C   r ( x,y ),
 
     wherein p(x, y) is the value of the pixel at coordinates (x, y) of the image-to-filter, Y(x, y) is the luminance at coordinates (x, y), C b (x, y) is the blue difference component, C r (x, y) is the red difference component from the YCbCr-image, and a and b are positive constants. This gives a higher contrast between the relevant colors. 
     The image-to-describe is built by converting the segmented image to a modified image with color indications to define the search area for signs and selecting the suitable descriptors. 
     The search area step  134  comprises four sub-steps ( FIG. 2 ), the sub-steps being further illustrated with an example in  FIG. 3 . 
     Strict Color Selection  231   
     Colors that are distinguishing for objects to be detected and recognized are selected, such as the colored outer band  311  of a traffic sign. The inner area  312  of a traffic sign is often white, which is hardly a distinguishing color in an image representing an outdoor scene. Therefore, no white areas  312  are initially selected. Next, the areas  321 ,  323  in the segmented image corresponding to the selected colors are selected for further searching. By limiting the colors that are added to the initial search area, this area is kept small. The smaller the area, the faster the remaining stages of the processing will be. 
     Area Filling  232   
     Areas  312  that are surrounded by the initial search area that resulted from step  231 , are included in area  333  as these areas possibly belong to traffic signs. This increases the reliability of the detection and recognition. 
     Blue Sky Skipping  233   
     If blue is one of the colors added to the initial selected colors, areas  323  likely to represent blue sky  313  are removed in order to limit the search area. 
     Edge Removal  234   
     The edges of traffic signs contain more information about the background than about the sign itself. Therefore, the edges are removed in the inner area  354  for higher reliability. 
     In the next pre-processing step, the integral image computation step  135 , the integral image of the image-to-filter is computed, because the usage of this image speeds up the processing stage. The integral image is given by 
               I   ⁡     (     x   ,   y     )       =       ∑     i   =   1     x     ⁢       ∑     j   =   1     y     ⁢     p   ⁡     (     i   ,   j     )                 
where I(x, y) is the value of pixel of the integral image with coordinates (x, y), and p(i, j) is the value the image has on pixel coordinates (i, j). Integral images are known to the person skilled in the art of image processing.
 
     The processing stage  140  uses the information determined in the pre-processing stage  130  to locate traffic signs in the image. The signs that are found are candidates that will undergo a final processing step in the postprocessor stage  150 . 
     The processing stage  140  comprises the feature extraction step  141 . Points that hold discriminative information about its surroundings are essential in the search for traffic signs. These points, called feature points, must be extracted from the image. The image used is the image-to-filter that was built by the pre-processing stage  130 . 
     For the extraction of these feature points, the Hessian blob detector is used. In this detector, first the Hessian of the scale space is computed. This scale space is given by
 
 L ( x,y ,σ)= G ( x,y ,σ)**Image( x,y ),
 
with ‘**’ denoting the two-dimensional convolution. Function G(x, y, σ) is the Gaussian function specified by
 
                 G   ⁡     (     x   ,   y   ,   σ     )       =       1     2   ⁢   π   ⁢           ⁢     σ   2         ⁢     ⅇ     -         x   2     +     y   2         2   ⁢     σ   2                 ,         
where σ 2  is the variance, which is in this case the scale of the scale space. This Gaussian function is optimal for scale space analysis. The Hessian of this function is computed by the matrix
 
                 H   ⁡     (     x   ,   y   ,   σ     )       =     [             L   xx     ⁡     (     x   ,   y   ,   σ     )               L   xy     ⁡     (     x   ,   y   ,   σ     )                   L   xy     ⁡     (     x   ,   y   ,   σ     )               L   yy     ⁡     (     x   ,   y   ,   σ     )             ]       ,         
in which L ab (x, y, σ) stands for the convolution of the second-order derivative to a and b of the Gaussian-filtered image. The blobs are extracted from the Monge-Ampère operator, which is the scale-normalized determinant of the Hessian matrix, as given by
 
det( H ( x,y ,σ))=σ 2 ( L   xx   L   yy   −L   xy   2 ),
 
with σ 2  being the scale normalization.
 
     The feature points are the maxima and minima in the filter results for several scales, which is spanned in a three-dimensional space. A threshold for the magnitude (absolute value) of the extreme values is set to limit the number of feature points found. This means that only magnitudes above the threshold are considered. The accuracy of the scale is increased by interpolation, for example, using spline interpolation. 
     The feature points do not disclose information about the underlying traffic signs. Therefore, in order to classify the feature points, a description of these points is made in the feature description step  142 . To describe the feature points, the vicinity of the point is used. The image from which the information about the vicinity is used, is the image-to-describe that was built by the pre-processing stage  130 . 
     The descriptor designed is the ‘color-presence descriptor’. The analyzed image region is divided in n×n sub-regions. In each sub-region, the colors are counted. Depending on the two colors that are mostly present in the sub-region, an informative number is given to the sub-region. All informative values of the sub-regions are combined into one descriptor. These sub-regions are the first layer in the hierarchy of templates. All sub-regions describe the small elements of the underlying traffic sign, and act as a small, partial or sub-template of a sign. 
     To enhance the descriptor, several representative values are computed. These representative values describe a general impression of the descriptor and are derived from the region around the feature point. More specifically, in the described embodiment, the analyzed image region is divided in just 2×2 sub-regions. In each sub-region the colors are again counted. The results of this counting determine an informative value describing the colors and give the representative values. 
     The feature matching step  143  of the method tries to match each descriptor to one or more descriptors from a library. Hence, a descriptor is matched to the library of small templates or descriptors from a library. To match a descriptor to a descriptor from the library, the number of differences is computed. If this difference number is smaller than the threshold given in the library, the descriptors are considered to match. 
     In this feature-matching step  143 , the second layer in the hierarchy of templates becomes clear. The descriptor, which is the combination of all sub-regions as described in the last section, gives the information of a part of the traffic sign. It is compared to parts of the traffic signs available in the library. 
     If all of the main values do not match with the main values of the library descriptor, they are considered not to match. This saves the number of differences that needs to be computed significantly. The library can even be divided into several libraries. Which library should be used can be determined from the main values of the descriptor. 
     A special property to reduce the influence of the background of the traffic signs is applied. A mask is used that belongs to each descriptor of the library, which indicates where the background pixels  410  would have a too large influence on the specific part of the descriptor. This concept is illustrated in  FIG. 4 , where background pixels  410  are labelled with ‘B’ and excluded from the comparison. 
     For the next step, the feature combining step  144 , the parameters for each match are stored. These parameters are the location of the feature point, the scale, and the ID of the matching descriptor of the library. 
     The matches  510  ( FIG. 5 ) of the last stage (small templates) are clues to the finding of complete traffic signs in the image. By combining these clues, traffic signs are actually located, for example by applying majority voting or a similar technique. 
     Before the matches  510  are used to locate possible traffic signs, the matches  510  are clustered by location. Only the matches  510  that belong to the same cluster can form traffic-sign candidates together. For every match  510 , a line  520  on which the center of the traffic sign can be is computed. The length of this line depends on the scale of the found feature point compared to the scale of the matching point from the library. Then, two matches  510  are combined by computing the location of the crossing of the two lines  520 , as illustrated in  FIG. 5 . If they cross each other, the proposed sizes for the traffic sign by both matches  510  are compared. If they are close, the center is stored. After all combinations of matches  510  are found, the centers are clustered to form the actual candidate traffic signs. A minimum of clustered centers is required to accept the candidate. This minimum depends on the size of the found traffic sign. 
     The combination that is created by this combining of the matches  510  indicates the third layer  630  ( FIG. 6 ) in the hierarchy of templates. This level is further exploited in the post-processing stage. 
     The output of this step and thus of the processing stage  140 , consists of candidate traffic signs with the location of the center, the size and the type (ID). 
     The final, or post-processing stage  150 , retrieves the candidate traffic signs from the processing stage  140 . To make the complete algorithm more reliable, these candidates are tested more thoroughly. As a result, false detections are rejected and correct detections get a value that indicates the accuracy. Also, the accuracy of the size and location of the correct detections are improved. The block diagram of the validation step is shown in  FIG. 7 , and the individual steps of this algorithm are described in the sequel. 
     The candidate traffic sign of the previous stage includes a center location and a size of the sign. Using these two parameters, a box fitted to contain the candidate traffic sign is determined in the set region step  710 , even if the proposed size or location are not perfectly known. This region is used for the remainder of the steps and will be referred to as the ‘search region’. 
     The region is already segmented during the pre-processing stage  130 . However, the segmentation results are improved by removing noise using noise reduction filters in the segmentation step  715 . This can be implemented by considering the surrounding pixels, and testing whether they have the same color. The color may change if too many surrounding pixels have a different color. 
     In the test color percentage step  720 , the percentages of the colors in the center of the search region are determined. These percentages are compared to the percentages of the original traffic sign. If the percentages differ too much, the traffic sign is rejected. 
     In the ‘find location and size’ step  725 , the third level  630  of hierarchical templates is used. To locate the possible traffic sign with more precision, slices are used. The library contains information about slices of the original traffic sign as a whole. These slices indicate the color transitions and the relative locations of these color transitions in both the horizontal and vertical direction. For several horizontal and vertical slices around the middle-point of the search region, the color transitions and relative locations are computed. The color transitions are compared with the ones of the slices of the library in order to determine the horizontal and vertical size and location of the traffic sign. The relative locations of the transitions indicate the horizontal and vertical size of the found sign. The number of slices matching to the library relative to the total slices tested is used to determine an intermediate likelihood indicator ranging from 0% (rather poor) to 100% (perfect). 
     During the previous step, an intermediate likelihood was obtained. If this likelihood is below some threshold, the traffic sign is rejected in the threshold step  730  and the remainder of the steps is skipped. By setting the threshold, the results of the validation step can be influenced. 
     If desired by the traffic sign found (this is stored in the library), the shape of the traffic sign can be tested using the size and location of the sign found in the test shape step  735 . This shape comes from a transition between two colors. The dimensions of the shape are detected using the information of this color transition. The shape is validated with a precise template of the shape that must cover a minimal amount of the color transitions. The shape is validated with a precise template of the shape that must cover a minimal amount of the colour transitions. 
     Using the location and the size of the traffic sign found, another color-percentage check is executed in the test color percentages step  740 . The percentages of all colors are calculated. Because of the known location and size, the thresholds for the different percentages are stricter than the center percentage check. Also, an upper limit for the color percentages is set. The thresholds depend on the percentages of the colors in the original traffic sign. If the sign is rejected, the following steps are skipped. 
     Using the knowledge about the center of the traffic sign and the size of the traffic sign, a likelihood indicator is computed in the compute likelihood step  745 . Therefore, a total descriptor of the found traffic sign is computed. This total descriptor represents the spatial color information of the sign. The computed descriptor is compared to the total descriptor in the library. The distance between the descriptors gives the obtained detection likelihood. 
     There are traffic signs that are look-a-likes of each other. Therefore, it is possible that more options are proposed by the previous step and that more than one option reaches this step during the validation. Additionally, one traffic sign could incidentally vote for itself with two candidates. To find out which sign is present in the image actually, or which candidate is the actual version of the sign, every traffic sign that reaches this compare traffic signs step  750  is compared to the signs already found. If two signs that are the same are in the same neighborhood, the weakest is removed. If the signs that overlap are different, the strongest remains and the weakest becomes an alternative. 
     When the traffic sign reaches the store traffic sign step  755 , it is stored. The stored parameters for each traffic sign are the type (ID), the likelihood, the location of the center, the sizes in the x-dimension and in the y-dimension and an indicator of the status of the found sign. The indicator tells whether it is an accepted or an alternative traffic sign. 
     In an additional step ( FIG. 8 ), a refinement of the results can be obtained by finding supplementary traffic signs. These signs, often white, are usually located underneath other traffic signs. By searching this area of each found traffic sign for such signs, most of these signs can be located. 
     In the test for other signs step  810 , the area underneath the found traffic sign is investigated. If another sign is already located in this area, there will not be any other sign, so also not any supplementary sign. In that case, the remainder of the steps is skipped. 
     Otherwise in the set region step  820 , the size and location of the found traffic sign are used to set the area in which the possible supplementary traffic sign should be. This area is called the search region in the following steps of the method. 
     In the segmentation step  830 , the search region is segmented indicating the pixels that have the color of the supplementary sign using the segmentation result of the pre-processing stage  130 , which is improved. This improvement contains noise removal and the adding of the possible text on the supplementary sign to the segmentation map. 
     In the case of supplementary traffic signs, there are several possible shapes they can have. However, all of them have an overlap in the upper half of the search region in the middle. In the subsequent test color percentage step  840 , after calculating the percentage of the pixels belonging to the sign in the segmentation map of this middle area, this percentage is compared to a threshold. If the percentage is too low, the algorithm is halted at this step. 
     To find the bounding rectangle in the locate rectangle step  850 , a rectangle is built using the segmentation map at first. Next, the image-to-filter is used to increase the accuracy of the last found side of the bounding box. The rectangle is built by trying to surround an—as large as possible—solid rectangle in the segmented image. The increase of accuracy is realized by computing differences, or parameters related to differences such as gradients, in the image-to-filter. These differences indicate edges that could not be seen in the segmented image. 
     In order to determine a likelihood indicator and to find out whether really a supplementary traffic sign is found, the percentage of the color of the sign in the bounding rectangle found is computed in the test color percentage step  860  and compared to a threshold. If the percentage is too low, the possible sign is rejected. 
     If the store traffic sign step  870  is reached, the supplementary sign is saved. This is done in the same way other traffic signs are saved. This means the ID indicating ‘supplementary traffic sign’, the likelihood indicator, which is the percentage of white found in the previous step, the location of the center and the size in the x-dimension and in the y-dimension. The indicator of the status of the found traffic sign is set to ‘accepted’. 
     Instead of using a training set to train the system, a parent figure is created in a specific embodiment of the disclosure, for the generation of the library. All information relevant for the algorithm is extracted from this parent figure. This means that this information must be able to cope with noise, small deformations, different scales, etc. This is accomplished by the steps in the method: the information from the library is not taken for granted, levels of differences are allowed. 
     In the library ( FIG. 9 ) a lot of information is stored. The steps of the method, including the information these steps require to be extracted from the parent figure, are the following:
         Feature matching  143 
           Descriptors for all feature points of the parent figure which can be used to match with the found descriptors   Masks for each descriptor   Thresholds for each descriptor   
           Feature combining  144 
           The direction to the center of the parent figure per feature point   The scale per feature point   Traffic sign ID of the parent figure per feature point   
           Validation  151 
           Color percentages of the parent figure   Localization: slices of the parent figure   Likelihood indicator: total descriptor of the parent figure   Size in the x-dimension and the y-dimension   Whether a specific shape check is desired   Whether a refinement search is desired   
               

     It should be noted that all embodiments described and shown herein are example embodiments. The person skilled in the art understands that many of the steps described for an embodiment may be replaced by alternative steps well known to the person skilled in the art. Furthermore, some steps are not essential, but result in an advantageous effect and are therefore comprised in one of the preferred embodiments. They may, however, be omitted without departing from the present closure. The scope of protection sought is defined by the claims. 
     In certain embodiments of the present disclosure, a method is provided comprising preparing an input image, locating at least one candidate traffic sign in the prepared input image, rejecting one or more false detections of the located at least one candidate traffic sign, and attributing a value that indicates an accuracy of one or more correct detections of the at least one located candidate traffic sign, wherein locating the at least one candidate traffic sign in the prepared input image comprises identifying a plurality of feature points in the prepared input image, constructing, using one or more processors, a descriptor for each of the plurality of feature points, wherein the descriptor includes a description of a vicinity associated with each of the plurality of feature points, providing a library comprising a hierarchy of a plurality of templates corresponding to a plurality of traffic signs, each one of the plurality of templates corresponding to a specific traffic sign and comprising a plurality of sub-templates that represent a part of the specific traffic sign, wherein each one of the plurality of templates comprises an image of the specific traffic sign to which it corresponds and the plurality of sub-templates each comprise a different part of the image of the specific traffic sign, matching the descriptor constructed for each of the plurality of feature points to the plurality of sub-templates in the library or to at least one of a plurality of corresponding descriptors derived from the plurality of sub-templates, combining the plurality of sub-templates matched with the descriptor constructed for each of the plurality of feature points to form the at least one candidate traffic sign, storing a location and a scale of each of the plurality of feature points for which a corresponding descriptor has been matched to a sub-template, computing a line that corresponds to a center of the at least one candidate traffic sign for the descriptor constructed for each of the plurality of feature points and matched to the plurality of sub-templates in the library that correspond to the at least one candidate traffic sign, if it is determined that two or more lines computed for the descriptor constructed for each of the plurality of feature points and matched to the at least one of the plurality of sub-templates in the library that correspond to the at least one candidate traffic sign cross, storing information related to the crossing of the lines as a candidate center of the at least one candidate traffic sign if the scale stored for the matches in said combination, are within a predetermined range, clustering the stored information related to the crossing of lines as a candidate center to determine possible centers, and combining the sub-templates corresponding to the cluster of possible centers to form the at least one candidate traffic sign. 
     Certain aspects include that the confidence indication of the detection/recognition is split into an indication for the at least one candidate traffic sign without a traffic sign border and a confidence indication of the at least one candidate traffic sign in which the traffic sign border is considered. 
     Certain aspects include that matching comprises comparing colors in the prepared input image to a color description in a template. 
     Certain aspects include that each of the plurality of templates defines a background mask, and wherein the background mask determines one or more areas that are ignored during matching, wherein the one or more areas that are ignored during matching comprises a group of pixels and/or a group of sub-pixels. 
     Certain aspects include that the stored information related to the crossing of lines as a candidate center are clustered to determine the possible centers and the sub-templates corresponding to the cluster of possible centers are combined to form the at least one candidate traffic sign after all combinations of matches corresponding to the at least one candidate traffic sign are determined. 
     In some aspects of the present disclosure, a non-transitory computer-readable medium is provided having embodied thereon computer executable instructions which, when executed by a computer, causes the computer to prepare an input image, to locate at least one candidate traffic sign in the prepared input image, to reject one or more false detections of the located at least one candidate traffic sign, to attribute a value that indicates an accuracy of one or more correct detections of the at least one located candidate traffic sign, to identify a plurality of feature points in the prepared input image, to construct a descriptor for each of the plurality of feature points, wherein the descriptor includes a description of a vicinity associated with each of the plurality of feature points, to provide a library comprising a hierarchy of a plurality of templates corresponding to a plurality of traffic signs, each one of the plurality of templates corresponding to a specific traffic sign and comprising a plurality of sub-templates that represent a part of the specific traffic sign, wherein each one of the plurality of templates comprises an image of the specific traffic sign to which it corresponds and the plurality of sub-templates each comprise a different part of the image of the specific traffic sign, to match the descriptor constructed for each of the plurality of feature points to the plurality of sub-templates in the library or to at least one of a plurality of corresponding descriptors derived from the plurality of sub-templates, to combine the plurality of sub-templates matched with the descriptor constructed for each of the plurality of feature points to form the at least one candidate traffic sign, to store a location and a scale of each of the plurality of feature points for which a corresponding descriptor has been matched to a sub-template, to compute a line that corresponds to a center of the at least one candidate traffic sign for the descriptor constructed for each of the plurality of feature points and matched to the plurality of sub-templates in the library that correspond to the at least one candidate traffic sign, if it is determined that two or more lines computed for the descriptor constructed for each of the plurality of feature points and matched to the at least one of the plurality of sub-templates in the library that correspond to the at least one candidate traffic sign cross, to store information related to the crossing of the lines as a candidate center of the at least one candidate traffic sign if the scale stored for the matches in said combination, are within a predetermined range, to cluster the stored information related to the crossing of lines as a candidate center to determine possible centers, and to combine the sub-templates corresponding to the cluster of possible centers to form the at least one candidate traffic sign. 
     Certain aspects include that a template in the library comprises an indication of the confidence of the detection/recognition. 
     Certain aspects include that the computer executable instructions which, when executed by a computer, further causes the computer to split the confidence indication of the detection/recognition into an indication for the at least one candidate traffic sign without a traffic sign border and a confidence indication of the at least one candidate traffic sign in which the traffic sign border is considered. 
     Certain aspects include that the computer executable instructions which, when executed by a computer, further causes the computer to compare colors in the prepared input image to a color description in a template. 
     Certain aspects include that each of the plurality of templates defines a background mask, and wherein the background mask determines one or more areas that are ignored during matching, wherein the one or more areas that are ignored during matching comprises a group of pixels and/or a group of sub-pixels. 
     Certain aspects include that the computer executable instructions which, when executed by a computer, further causes the computer to cluster the stored information related to the crossing of lines as a candidate center are to determine the possible centers, and to combine the sub-templates corresponding to the cluster of possible centers to form the at least one candidate traffic sign after all combinations of matches corresponding to the at least one candidate traffic sign are determined. 
     In some aspects of the present disclosure, an apparatus is provided comprising one or more processors, and a memory storing instructions which, when executed by the one or more processors, causes the one or more processors to prepare an input image, to locate at least one candidate traffic sign in the prepared input image, to reject one or more false detections of the located at least one candidate traffic sign, to a value that indicates an accuracy of one or more correct detections of the at least one located candidate traffic sign, to identify a plurality of feature points in the prepared input image, to construct a descriptor for each of the plurality of feature points, wherein the descriptor includes a description of a vicinity associated with each of the plurality of feature points, to provide a library comprising a hierarchy of a plurality of templates corresponding to a plurality of traffic signs, each one of the plurality of templates corresponding to a specific traffic sign and comprising a plurality of sub-templates that represent a part of the specific traffic sign, wherein each one of the plurality of templates comprises an image of the specific traffic sign to which it corresponds and the plurality of sub-templates each comprise a different part of the image of the specific traffic sign, to match the descriptor constructed for each of the plurality of feature points to the plurality of sub-templates in the library or to at least one of a plurality of corresponding descriptors derived from the plurality of sub-templates, to combine the plurality of sub-templates matched with the descriptor constructed for each of the plurality of feature points to form the at least one candidate traffic sign, to store a location and a scale of each of the plurality of feature points for which a corresponding descriptor has been matched to a sub-template, to compute a line that corresponds to a center of the at least one candidate traffic sign for the descriptor constructed for each of the plurality of feature points and matched to the plurality of sub-templates in the library that correspond to the at least one candidate traffic sign, if it is determined that two or more lines computed for the descriptor constructed for each of the plurality of feature points and matched to the at least one of the plurality of sub-templates in the library that correspond to the at least one candidate traffic sign cross, to store information related to the crossing of the lines as a candidate center of the at least one candidate traffic sign if the scale stored for the matches in said combination, are within a predetermined range, to cluster the stored information related to the crossing of lines as a candidate center to determine possible centers, and to combine the sub-templates corresponding to the cluster of possible centers to form the at least one candidate traffic sign. 
     Certain aspects include that a template in the library comprises an indication of the confidence of the detection/recognition. 
     Certain aspects include that the memory is further configured to store instructions which, when executed by a computer, causes the one or more processors to split the confidence indication of the detection/recognition into an indication for the at least one candidate traffic sign without a traffic sign border and a confidence indication of the at least one candidate traffic sign in which the traffic sign border is considered. 
     Certain aspects include that the memory is further configured to store instructions which, when executed by a computer, causes the one or more processors to compare colors in the prepared input image to a color description in a template. 
     Certain aspects include that each of the plurality of templates defines a background mask, and wherein the background mask determines one or more areas that are ignored during matching, wherein the one or more areas that are ignored during matching comprises a group of pixels and/or a group of sub-pixels. 
     Certain aspects include that the memory is further configured to store instructions which, when executed by a computer, causes the one or more processors to cluster the stored information related to the crossing of lines as a candidate center are to determine the possible centers, and to combine the sub-templates corresponding to the cluster of possible centers to form the at least one candidate traffic sign after all combinations of matches corresponding to the at least one candidate traffic sign are determined. 
     Various other modifications and alterations in the structure and method of operation of this disclosure will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments of the present disclosure. Although the present disclosure has been described in connection with particular embodiments, it should be understood that the present disclosure as claimed should not be unduly limited to such particular embodiments. It is intended that the following claims define the scope of the present disclosure and that structures and methods within the scope of these claims and their equivalents be covered thereby.