Patent Publication Number: US-11398098-B2

Title: Real time traffic sign recognition

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/376,438 filed on Apr. 5, 2019, which is a continuation of U.S. patent application Ser. No. 15/395,141, filed on Dec. 30, 2016, now U.S. Pat. No. 10,255,511, which claims priority to Indian Provisional Application No. 201641000153, filed on Jan. 4, 2016, each of which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The technical field of this invention is image processing. 
     BACKGROUND 
     Traffic sign recognition (TSR) is a technology which makes vehicles capable of recognizing the traffic signs appearing in the vicinity of the driving path. TSR systems form an important part of the ADAS (advanced driver assistance systems) that is currently being deployed in the cars of today. It is a classic example of rigid object detection. TSR systems depend on forward facing image sensors. Current TSR systems are aimed to assist the driver in the driving process. But, in the future, TSR systems will play a very crucial role in the functioning of autonomous cars. 
     Computers face a lot of challenges in identifying traffic signs in images due to the following reasons: 
     Within-class variability: the same traffic sign in the real world can give rise to different images due to:
         a. Different viewing positions and different distances between the camera and traffic sign positions, and   b. Photometric effects: positions of multiple different light sources, their color, distribution of shadows, and view obstruction by objects present near the traffic signs.       

     Between-class similarity: different classes of traffic signs may look very much alike. 
     Background objects in cluttered urban environments also pose a challenge. 
     Motion blur in images. 
     Faded, bent, and dirty sign boards. 
     Adverse weather conditions like rain and snow. 
     Traffic signs may also be slightly different from country to country. For example, speed limit traffic signs in some European countries are round with a red circle boundary, while in the US they are rectangular in shape. 
     SUMMARY 
     A real time Traffic Sign Recognition (TSR) system is described comprising of a preprocessing stage to identify image regions containing a traffic sign, a localization stage to accurately locate the sign within the image, a categorization stage to categorize the located sign into one of the sign categories, and a temporal smoothening stage remove noise and false detections due to noise. 
     In accordance with one aspect of this disclosure, an apparatus includes an input to receive data corresponding to an image of a captured scene, a processor, and a memory to store instructions. Execution of the instructions by the processor cause the processor to identify one or more approximate locations of interest in the image based on extracting color information from the image, process each of the one or more approximate locations of interest in the image to identify one or more localized windows in the image in which a traffic sign is present, for each of the one or more localized windows, determine classification information of the respective traffic sign present in the localized window, and output information that identifies, for each traffic sign present in the captured scene, a traffic sign type based on the classification information. The extraction of the color information includes extracting, from the image, an initial binary mask for a respective color of a plurality of colors and then applying to each of the initial binary masks a morphological opening operation to produce a respective final binary mask for each of the initial binary masks. 
     In accordance with another aspect of this disclosure, a system includes an electronic apparatus that has an input receive data corresponding to an image of a captured scene and processing circuitry. The processing circuitry is configured to identify one or more approximate locations of interest in the image based on extracting color information from the image by extracting, from the image, an initial binary mask for a respective color of a plurality of colors, and then applying to each of the initial binary masks, a morphological opening operation to produce a respective final binary mask for each of the initial binary masks, process each of the one or more approximate locations of interest in the image to identify one or more localized windows in the image in which a traffic sign is present, for each of the one or more localized windows, determine classification information of the respective traffic sign present in the localized window, output information that identifies, for each traffic sign present in the captured scene, a traffic sign type based on the classification information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other aspects of this invention are illustrated in the drawings, in which: 
         FIG. 1  illustrates a system block diagram of a TSR system in accordance with one example embodiment; 
         FIG. 2  illustrates a segmentation stage of the TSR system of  FIG. 1 ; 
         FIG. 3  shows a flow chart of an extended radial symmetry transform (ERST) step; 
         FIG. 4  illustrates the computed feature planes; 
         FIG. 5  shows various positions of the models inside the image; 
         FIG. 6  illustrates a depth=2 decision tree; 
         FIG. 7  illustrates a block diagram of the temporal smoothening engine; and 
         FIG. 8  shows a flow chart of the temporal smoothening engine. 
     
    
    
     DETAILED DESCRIPTION 
     A four stage TSR algorithm is shown as described below. It is also shown pictorially by way of the system block diagram in  FIG. 1 . 
     Stage 1: Preprocessing Stage  101   
     Identify the approximate image regions having traffic signs without missing any traffic sign in input images. 
     Stage 2: Accurate Localization Stage  102   
     Stage 2a: Extract features from input images in feature computation block  103   
     Stage 2b: Accurate localization of the region(s) of the input image that contain the traffic sign(s) using classifier in  104 . 
     Stage 3: Classification Stage  105   
     The windows localized by stage 2 are categorized into one of a plurality of categories. 
     Stage 4: Temporal Smoothening Stage  106   
     This stage is meant to remove the noisy detections and noisy classification that have been obtained from stage 3. 
     The preprocessing stage  101  works on the input image and is aimed at reducing the complexity of the TSR system by reducing the amount of data that is processed by subsequent stages. It is implemented in two steps: 
     (1) Extract color cues to find possible locations of traffic signs 
     (2) A shape detector then uses these color cues to identify image locations having traffic signs. 
     As shown in  FIG. 2 , the RGB (red, green, blue) input image received at the preprocessing stage is spatially subsampled by a factor of four in both directions in block  202 , and then converted to a YUV (luminance/chrominance) image in block  203 . This is to keep the complexity of the segmentation stage  102  under manageable limits for an embedded system implementation. 
     Contrast stretching is done in block  204  by using histogram equalization on the Y plane. This improves the performance of the algorithm in many low contrast input images. 
     Red, Blue, Yellow and White binary masks are extracted by thresholding in the YUV color space (1 mask for each color) in block  205 . 
     Morphological opening (e.g., erosion followed by dilation) is then applied in blocks  206  for each of these binary masks. 
     The masks are combined in block  207   
     The binary masks are used by an extended radial symmetry transform (ERST) as shown at block  208 . ERST block  208  detects circles, triangles, squares, and octagons in the input images by performing voting for the gradients present in regions of the masks. 
       FIG. 3  shows the flow chart of an ERST process performed by the ERST block  208 . 
     In step  301 , a gradient map for an entire input image in grey scale is computed using a Sobel operator. 
     In step  302 , the binary masks obtained from color space thresholding (block  205  of  FIG. 2 ) act as (color) cues for this stage. 
     The gradients that are less than a threshold are zeroed out in step  303  and are not considered for later stages. 
     The voting is performed in a 3D accumulator array(x,y,r) at step  304 . One 3D accumulator array is maintained for each shape (e.g., circle, square, triangle, and octagon). 
     Voting (incrementing procedure of accumulator cells) is performed only for the gradient (edge) points for which the binary value in the mask is non-zero. 
     After voting finishes for the entire image in step  305 , the top ‘N’ peaks in each accumulator are used in step  306  to determine the position and radius of the circle/polygon at that point. 
     Feature extraction Stage 2a (block  103 ) is performed by: 
     (1) For each input image, an image pyramid is prepared. The number of image scales used is dependent on:
         (a) Maximum and minimum traffic sign size to be detected,   (b) Input image dimensions, and   (c) Complexity, accuracy tradeoff considerations.       

     (2) Aggregate Channel Features (ACF) planes are computed for every scale of each image, as shown in  FIG. 4 . 
     ACF is a collection of 10 feature planes of 3 channels comprising of original pixels of YUV space, 1 Gradient magnitude channel and 6 orientations channels of “histogram of oriented gradients” (HOGS). 
     Each of HOG orientation channel used as a part of ACF is computed from a cell size of 4×4 pixels without any overlap between cells and without block normalization. 
     Traffic sign localization Stage 2b is performed by: 
     (1) An ADA boost (Adaptive Boosting) classifier is used for this localization. Boosting is an approach to machine learning based on the idea of creating a highly accurate prediction rule by combining many relatively weak and inaccurate rules. 
     (2) 1024 number of decision trees of depth 2 act as weak classifiers for ADA boost. A single weak classifier is depicted in  FIG. 6 . 
     Features computed from 32×32 pixel blocks of images (known as a model) are used as inputs to the classifier (block  104 ). The model is made to step by 4 pixels (both horizontal and vertical) on each image and each scale, as shown in  FIG. 5 . At each position of the model  501  a feature vector of size 640 pixels is computed using the feature planes. 
     Feature vectors obtained in this manner from training images are used for training the ADA boost classifier (block  104 ). Training is done in four stages with 32, 128, 256, 1024 weak classifiers used in each stage. Boot strapping is used in each stage to strengthen the hypothesis. 
     The feature vector of size 640 pixels is fed to the ADA boost classifier. The ADA boost returns a real number which is binary thresholded to decide if a traffic sign is present or not. Note that the localization procedure is only a binary decision procedure where it is decided if a traffic sign is present or not. Actual classification (categorization to specific class) is done in the next stage. 
     Traffic sign classification Stage 3 (block  105 ) is done by: 
     The windows that are marked as containing traffic signs in Stage 2b are passed to the traffic sign classification stage (block  105 ) for categorization. Stage 2b is designed with maximum sensitivity in mind, i.e. no valid traffic sign should be missed but few false positives are acceptable. These false positives are filtered out by Stage 3. 
     The feature vector used in Stage 3 is of size 2992 pixels in one example and is used as an input to a Linear Discriminant Analysis (LDA) classifier. 
     LDA relies on minimization of a Mahalanobis distance between a feature vector and the mean vector of various classes. The Mahalanobis distance of an observation
 
 x =( x   1   ,x   2   ,x   3   , . . . ,x   N ) T  
 
     from a group of observations with mean
 
μ=(μ 1 ,μ 2 ,μ 3 , . . . ,μ N ) T  
 
     and covariance matrix S is defined as:
 
 D   M ( x )=√{square root over (( x −μ) T   S   −1 ( x −μ)}.
 
     The minimization of the Mahalanobis distance is mathematically equivalent to minimization of the below function:
 
 g   i ( x )= w   i   t   x+w   i0  
 
     where g i (x) is a cost function for class ‘i’, 
     w i  is a weight vector for class ‘i’, 
     w i0  is bias for class ‘i’, and 
     x is a vector of size 2992 pixels. 
     w i  and w i0  are pre-computed (during training) and are different for different classes. For a given feature vector x, g i (x) is computed for each class and the feature vector is associated with the class that gives the minimum value of the function g(x). 
     Temporal smoothening Stage 4 (block  106 ) is performed by: 
     Removing the noisy detections and noisy classification that have been obtained from the earlier stages. This stage is present only when the input is a sequence of images that form a part of a video. 
     An example of a temporal smoothening engine is conceptually depicted in  FIG. 7 . The inputs to the temporal smoothening engine are: 
     (1) The descriptors of detection windows  701  (position and dimensions) obtained from stage 2, and 
     (2) Class id&#39;s  702  that are associated with each of these detection windows obtained from stage 3. 
     The temporal smoothening engine internally maintains a history of the detection windows. This history is empty at the start of the sequence of pictures and is updated after every picture. A decision logic block inside the temporal smoothening engine looks at the inputs and the history before finalizing the windows and its associated class. 
     The temporal smoothing engine may use the Jaccard coefficient to measure degree of similarity between windows detected in the current picture and the windows stored in the history. The Jaccard coefficient J(A,B) between two windows A and B is defined as follows, 
     
       
         
           
             
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     The numerator term denotes the area under intersection and the denominator term denotes the area in the union of the two windows. 
     The details of the temporal smoothening engine are shown in  FIG. 8 . Inputs to the temporal smoothing engine are the detection windows (det_win[ ])  801  output from stage 2 (block  102 ) of the TSR algorithm, and the class id (id[ ])  802  for each detection window. In step  803 , hist[ ] is the state memory that is built when a new picture is processed. The Jaccard coefficient is computed in step  804  for every pair of windows, where each pair includes one window selected from hist[ ] and one window selected from det_win[ ]. In step  805  det_idx is set to zero. 
     Then, in step  806 , the window within hist[ ] that gives the maximum Jaccard coefficient value (J_max) when paired with the selected window from det_win[ ] (referred to as det_win[det_idx]) is determined. This window is referred to as hist[best_match_det_idx]. 
     If J_max is equal to or greater than 0.5 in decision step  807 , then hist[best_match_det_idx] is set as equal to det_win[det_idx], and id[det_idx] is associated with that same entry in hist[ ] at step  808 . If J_max is less than 0.5 at decision step  807 , then det_win[best_match_det_idx] is added to hist[ ] as a new entry, and id[best_match_det_idx] is stored with the same entry of hist[ ] in step  809 . 
     At decision step  810 , we determine if all entries of det_win[ ] have been processed. If not, det_idx is incremented in step  811 , and the flow returns to step  806 . If all entries of det_win[ ] have been processed, then all hist[ ] entries that have not been updated are deleted in step  812 . 
     The outputs of temporal smoothening engine are a final set of detection windows  813  and final class identifications  814 , which are used as the final outputs of the TSR system. 
     While this specification contains many specifics, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results unless such order is recited in one or more claims. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments.