We introduce in this paper, a shadow detection and removal method for moving objects especially for humans and vehicles. An effective method is presented for detecting and removing shadows from foreground figures. We assume that the foreground figures have been extracted from the input image by some background subtraction method. A figure may contain only one moving object with or without shadow. The homogeneity property of shadows is explored in a novel way for shadow detection and image division technique is used. The process is followed by filtering, removal, boundary removal and removal validation

As'ari, M. A.

Sheikh, U. U.

Syed Abu Bakar, Syed Abdul Rahman

Universiti Teknologi Malaysia Institutional Repository

2007 IEEE International Symposiumon Signal Processing and Information TechnologyObject's Shadow Removal with Removal ValidationM.A. As'aril, U.U. Sheikh2, S.A.R. Abu-Bakar3Computer Vision, Video and Image Processing (CvviP) Research GroupDept. of Microelectronics and Computer EngineeringFaculty of Electrical EngineeringUniversiti Teknologi Malaysia81310 UTM Skudai, Johor, MalaysiaPhone: +6075535274, Fax: +6075566272E-mail: amirulmukmininjbgyahoo.com', uus831 gyahoo.com2, syedgfke.utm.my3Abstract- We introduce in this paper, a shadow detection and global thresholding process is applied to the resultant imageremoval methodfor moving objects especially for humans and (after image division) in order to get the shadow's blob. Thevehicles. An effective method is presented for detecting and authors claim their method to be more generic in detecting andremoving shadows from foreground figures. We assume that removing shadows.the foreground figures have been extracted from the input The shadow detection method developed by [5] isimage by some background subtraction method. A figure may applied to gray level images taken by a stationary camera. Thecontain only one moving object with or without shadow. The authors used the Canny edge detector to both the foregroundhomogeneity property ofshadows is explored in a novel way and background figures (is mapped to the foreground'sfor shadow detection and image division technique is used. shape). After that, image subtraction is performed on theThe process is followed by filtering, removal, boundary foreground edges and background edges to extract the object'sremoval and removal validation. edges. Object recovery process is applied to the object's edgesto recover object shapes on the basis of the information in theKeywords - Shadow removal, homogeneity, umbra, object's edges and attributes of shadow. There are three rulespenumbra or attributes of shadow as defined by authors. Firstly, brightforeground pixels is preserved because they are impossible tobelong to shadows. Secondly, foreground pixels withI. INTRODUCTION attributes different from the attributes of shadow are preservedand lastly, foreground pixels nearby object edges areShadow is a common problem that one could preserved.encounter in motion estimation of daytime traffic scenes. The approach described in [6] is applied for human orShadows can cause object merging, object shape distortion pedestrian and it is based on priori information which is objectetc., causing error in object tracking and classification. Many moment and object orientation. In order to precisely removeresearchers had proposed shadow detection or removal based the unwanted shadows, this paper presents a histogramon priori information, such as the geometry of the scene or the projection method to separate each pedestrian from movingmoving objects and the location of the light source while region first. Then, a coarse-to-fine approach is applied forothers are based on shadow attributes. There are three main detecting the boundaries between the pedestrian and itsshadow attributes, firstly, shadows or moving shadows are shadow. At the coarse stage, a moment-based method isattached to their respective obstruction object for most of the applied to estimate the orientation of the detected pedestrian.time, secondly, transparency which is that shadow always According to the orientation and silhouette features of themakes the region it covers darker and lastly, homogeneity detected regions, a rough approximation of the exact shadowwhich is the ratio between pixels when illuminated and the area can be detected. At the fine stage, the roughsame pixels under shadows can be roughly linear. approximation of the shadow region is further refined throughMethod that has been proposed in [1], [2], [3] and [4] Gaussian shadow modeling. The major difficulty in shadowis an image division technique (between background and modeling is the choice of the proper model, which can reflectforeground). Image division is used because it highlights the various appearances of shadows at different orientation andhomogeneity property or attributes of shadow. For [2], after lighting.the image division process, a multi-gradient (horizontal, The algorithm explained in [7] is almost similar to [6]vertical and diagonal edges) operation is performed on the and it is also based on priori information plus a little bit ofresultant image to remove penumbra region and for [3], edge shadow attributes. The authors try to locate object-shadowgradient operation is applied after the division process to boundary in order to detect the shadow. This was done bydecide the shadow's blob. For [1], after the image division creating one or more straight lines to approximate theprocess, adaptive thresholding process is used to remove the boundary between vehicles and their associated shadows.umbra region and projection histogram analysis is applied to These lines are located in the image by exploiting both localthe resultant image to remove the penumbra region. In [4], information (e.g. statistics in intensity differences) and global978-1 -4244-1 835-0/07/$25.00 ©2007 IEEE 841Authorized licensed use limited to: UNIVERSITY TEKNOLOGI MALAYSIA. Downloaded on December 29, 2009 at 23:35 from IEEE Xplore.  Restrictions apply. information (e.g. principal edge directions). The authors saidthis method does not assume a particular lighting condition, Output fromand required no human interaction or parameter training. backgroundExperiments on practical real-world traffic video sequencesdemonstrate that this method is simple, robust and efficientunder traffic scenes with different lighting condition. Object's Blob Background'sII. MOTIVATION0-Image DivisionMany techniques which are based on homogeneityproperty of shadows assume that ratio between pixels whenilluminated and that are when subjected to shadow is constant. Thresholding &But, authors in [1] said that the ground truth data show that theratio is highly dependent on illumination in the scene andhence shadow detection will not be effective in case if theratio is assumed to be constant. In other words, the ratio is Filteringalways changing due to different illumination for differentscenes. That is why; the authors in [1] proposed an adaptivethresholding technique to improve or to solve the problem. BoundaryMost of the researchers that develop shadow Removaldetection based on this property (homogeneity) or imagedivision technique just assume the ratio is always constant anduse fix threshold value for the thresholding process (global Valthresholding). In addition, the majority of researchers that useimage division technique always use an additional technique lafter the image division process because the image divisionprocess is used to highlight the homogeneity property of Resultshadow especially the umbra region but not penumbra. That iswhy; they have proposed so many additional processes inorde to rmve thepoenumarn suchoas multigrad in Figure 1. Overall algorithm of the proposed shadow removal techniqueorder to remove the penumbra region such as multi-gradientanalysis [5] and projection histogram analysis [1].B. ThresholdingIII. THE ALGORITHMThe purpose of thresholding is to decide the shadow'sThe flowchart in figure 1 shows the main algorithm of blob in the resultant image after the image division processthe project that has been proposed and the resultant image for (Img-Div). Making use of findings from some researchersevery process is shown with an example in figure 4. It has [e.g 1,2,3,4], most of the existing approaches mark pixelsbeen assumed that the input (object's blob and background's lying between a certain range of value in the division image asblob) is obtained from some background subtraction. All belonging to a shadow.process in figure 1 is explained in the following sections. In [2], the authors set pixels value range of 50-80(t min = 50, t_ max = 80) as belonging to shadow pixels.A. Image Division However, the range is highly dependent on the illumination inthe scene. In addition, different scenes also produce differentlevel at illumination. Authors in [1] had proposed an adaptiveIn this process, the object's blob, ob(x, y), {x, y E 7Z2} threshold in order to solve this type of problem. However, thisis divided with the background's blob, bk(x, y), {x,y E 7Z2} technique has been tested with a few video samples and itIt has been said before that the purpose of image division is to seems that this technique does not work effectively comparedhighlight the homogeneity property of shadows. Resultant to a fixed threshold. It is because the range of adaptiveimage after the division process is multiplied with a constant threshold depends on the average value of the division imagefor the purpose of increasing the signal of the resultant image. and this will cause that the threshold value (range) thatIn this case,the constant value is100(Eq.]).Theresultofathisbelongs to the shadow change randomly. Because of thisprocess is define as Img_Div(x, y). problem, in this proposed technique, the range has been setaccording to the scene (Eq.2) and this is done by studying theob(x, y) histogram of the division image over a few samples.Img_Div(x,y) = bk(x,) X 100,Vx E X,Vy E Y (1)842Authorized licensed use limited to: UNIVERSITY TEKNOLOGI MALAYSIA. Downloaded on December 29, 2009 at 23:35 from IEEE Xplore.  Restrictions apply. Img Th = {1, t_min < Img-Div < t_max V(x y) (2) The purpose of the Percentage Checking process is toImg-7h = O, otherwise ' ' check whether the removal process was correct or not. This isdone by checking the percentage of area that has beenremoved in the removal process (Eq.3), where BR representC. Filtering the percentage of area that has been removed over the area ofwhole object's blob. Based on the study and analysis ofThe purpose of filtering is to enhance the resultant sample images, the shadow removal is correct if theimage after the thresholding process (Img_Th) and to find the percentage value is within a range that is dependent on a scenebiggest blob which is predicted as the shadow's blob or (Eq.4), where RV, percent-min and percent_max representshadow region. Filtering process include filling, erosion and removal validation result, minimum percentage and maximumdilation to enhance the image and labeling to predict the percentage (the range). This percentage range will beshadow. It is assumed that the biggest blob after the labeling explained later in section IV. If the percentage value does notprocess or connected component process as a shadow region. fall in that range, it is assumed that the removal did not workcorrectly.D. Boundary Removalarea that has been removedBR = object's blob area x 100 (3)The purpose is to remove the penumbra region ofshadow, or in other words to remove the shadow's boundary. true, percent_mi . BR . percentmaxThe first step in this process is to get the coordinates of the RV = false otherwise(4)boundary (object's blob). This is also called as boundarytracing process. After that, each boundary pixel and itsneighbor is checked whether it is a shadow pixel or not. In this The second sub process is the Vertical Scan processcase, neighbor pixels that are only located in the horizontal, which will check which part of the object's blob is predictedvertical, and diagonal (45 and -45 degree) of the boundary as a shadow region. In the Filtering process, it is assumed thatpixel with certain offset (range between neighbor pixels and the biggest blob after that labeling process is the shadow'sboundary pixel) are checked. region. However based on the study of input samples,Figure 2 shows the boundary pixel (grey colored pixelsometimes, the second biggest blob iS the correct shadowthat is located at the centre) and the neighboring pixels (black setimes, thes biggest blob is the crect shadowregion and the biggest blob iS not a shadow region. Figure 3colored pixels that are located at the horizontal, vertical and shows an example where the Filtering process has done adiagonal of the boundary pixel) with an offset value equal to.. .wrong prediction, and based on the analysis, this biggest blobtwo. So, if the boundary pixel or its neighboring pixels are (wrong predicted shadow's region) is always located at thedetected as shadow pixels, the boundary is assumed as a pixel center of the object's blob.from the shadow's boundary. At this stage, the boundary pixel Sand its neighboring pixel is removed (turn the pixel into white pfm tu the entid of the o et's al st tocolor). ~~~~~~~~~~~perfonmed though the centroid of the object's blob just tocolor), make sure that the predicted shadow's region is not located atthe center of object's blob. However, the Vertical Scanprocess can only be applied on certain scenes. Some scenesare not suitable because it will only cause a poorer result.g | | 9Incorrect OutputFigure 2. Neighbor pixels checked around a boundary pixel Input Image After Image Divisionwith an offset of two and ThresholdingProcessCorrect OutputF. Removal ValidationFigure 3. Vertical scan to determine correct removalRemoval validation process consists of two subprocesses which are the Percentage Checking process and theVertical Scan process.843Authorized licensed use limited to: UNIVERSITY TEKNOLOGI MALAYSIA. Downloaded on December 29, 2009 at 23:35 from IEEE Xplore.  Restrictions apply. Object's Blob Background's Blob formulas that are applied in this analysis where the PV, andPSS represent the percentage of correct result from a videosamples, number of video samples in a scene and percentageof correct removal in a scene.no. of frames with correct result~~~Result after PV =(5image division & no. of frames in video samplethresholding~~~~process EN PPSS N (6)N-] filteringprocessremoval 3/ ~~process _1WIIIl1(a) (b)BoundaryremovalresulItFigure 4. Shadow removal process(c) (d)IV. EXPERIMENTAL RESULTS AND DISCUSSIONIn order to demonstrate its usability, the proposedshadow detection and removal have been applied on differenttypes of video sequences and different types of scenes. Infigure 5, three results of the shadow detection and removal are (e) (f)presented. In the first scene, the input is shown figure 5a andthe output is shown figure 5b. For this scene, the Vertical Scan Figure 5. Shadow removal results for several scenes (before a, c, e andprocess is disabled because the object orientation is not ' 'suitable application of process. This is also true for the thirdscene (figure 5e and 5f). In the first scene, the range ofshadow pixel has been set from 20 to 50 and the percentage TABLE Ivalue for the Percentage Checking process (see Removal SCENES AND PERCENTAGE OF CORRECT REMOVALValidation process) is 8% to 45%. SCENE Percentage of CorrectFor the second scene (figure 5c and 5d), the Vertical Removal (%)Scan process is applied to prevent errors that have been ISt 100.00mentioned before, in figure 4. The shadow pixel range is set 9rd1from 60 to 98 and percentage value for the Percentage 3 69.18Checking process is 8%-33%. For the third scene, shadowpixel range is from 45 to 75 and the percentage ranges aresimilar to the second scene. V. CONCLUSIONTable 1 show the accuracy or the percentage of correctresults based on the scenes that have been shown in figure 5. A novel method to detect attached shadow has beenIn each scene, the vehicle is monitored and analyzed for anresented. The, targret senuence 1S a dnvfime trnffic- sceneperiod of time and the overall success rate is calculated. This prsne.Tetgtsquceiadyim tafcseecorrctesul frm evry nvolving different types of orientation and scenes. Figure 5is calculatedbythe percentage o shows the result with different types of object size, orientationvideo sample, P1/1 {i = 1.. N} (by getting the number of n cnsframes that have the correct result over the number offrames) ansce.and then, to get the average percentage of correct result from Wrigwt udo mgsa nu svrvideo samples in the same scene (PSS). Eq. 5 and Eq. 6 are the chlegn,a h luiainntol hne lwya h844Authorized licensed use limited to: UNIVERSITY TEKNOLOGI MALAYSIA. Downloaded on December 29, 2009 at 23:35 from IEEE Xplore.  Restrictions apply. daytime progresses but may change rapidly due to the [3] A. Bevilacqua, M. Roffilli, "Robust denoising andchanging weather conditions as well as due to the passing moving shadows detection in traffic scenes." Proc. ofobjects. So, the proposed technique especially the Removal IEEE Computer Society Conference on ComputerValidation process will assist the removal process in order to Vision and Pattern Recognition, Kauai Marriott,detect whether there are shadows present in an image. Hawaii, pp 1-4, 2001.The proposed method improves object detection, [4] Falah E. Alsaqre, Yuan Baozong, "Moving Shadowswhich is a very critical task in video surveillance and vision- Detection In Video Sequences." Proc. of 7th ICSP 04,based traffic monitoring systems. V2, pp 1306-1309, 2004.[5] J.M. Wang, Y.C. Chung, C. L. Chang, S. W. Chen,"Shadow Detection and Removal for TrafficVI. ACKNOWLEDGMENT Images." Proc. of IEEE International Conference onNetworking, Sensing and Control, Taipei, Taiwan,This project has been made possible through the VI, pp 649-654, 2004.research contract grant managed by Universiti Teknologi [6] Chia-Jung Chang, Wen-Fong Hu, Jun-Wei Hsieh,Malaysia (UTM) under the vote 68305. The authors would Yung-Sheng Chen, "Shadow Elimination forlike to express their gratitude for this support to UTM. Effective Moving Object Detection with GaussianModels." Proc. of 16th International Conference onVII. REFERENCES Pattern Recognition (ICPR '02), V2, pp 540-543,2002.[1] Mohamed Ibrahim M, Anupama R, "Scene Adaptive [7] Angie W. K. So, Kwan-Yee K. Wong, Ronald H. Y.Shadow Detection Algorithm." Transactions on Chung, and Francis Y. L. Chin. "Shadow DetectionFor Vehicles By Locating The Ob ect-ShadowEngineering, Computing and Technology, V2, Boury Prc. LofASE on JecoSialDecember, pp 88-91, 2004. Boundary." Proc. of IASTED Conference on Signal[2] Alessandro Bevilacqua, "Effective Shadow Detection and Image Processing (SIP 2005), pp 315-319, 2005.in Traffic Monitoring Applications." Journal ofWSCG, VI1, pp 57-64, 2003.845Authorized licensed use limited to: UNIVERSITY TEKNOLOGI MALAYSIA. Downloaded on December 29, 2009 at 23:35 from IEEE Xplore.  Restrictions apply. 

Object's shadow removal with removal validation

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Object's Shadow Removal with Removal Validation

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Object's shadow removal with removal validation

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