Adaptive Moving Cast Shadow Detection by Integrating Multiple Cues

Transcription

1 Chinese Journal of Electronics Vol.22, No.4, Oct Adaptive Moving Cast Shadow Detection by Integrating Multiple Cues LING Zhigang, LU Xiao, WANG Yaonan and HE Xi (College of Electrical and Information Engineering, Hunan University, Changsha , China) Abstract Moving cast shadow detection and removal is a key step for accurate object detection in intelligent transportation system. This paper proposes a robust cast shadow detection algorithm by integrating multiple cues. Firstly, a weak shadow detector is adopted to detect these potential shadow pixels; Then three adaptive shadow estimators are designed and cascaded to integrate texture, chromaticity, brightness as well as spatial-temporal context for eliminating the object pixels so that this algorithm can robustly detect the moving cast shadow in the various environments; Lastly, spatial adjustment is employed to verify the shadow detection results of these three shadow estimators. Experimental results on indoor and outdoor video sequences show that this proposed algorithm can robustly detect moving cast shadow and rapidly adapt to variations in traffic surveillance scenarios. Key words Multiple cues, Adaptive shadow estimators, Texture difference, Chromaticity and brightness distortion, Moving cast shadow detection. I. Introduction Moving objects detection is the basic step for further analysis of video in intelligent transportation system intelligent visual surveillance systems, etc. However, cast shadows of these moving objects including vehicles, pedestrian and etc. tend to be misclassified as the foreground regions because they share the same movement pattern and the similar magnitude of intensity change as that of the foreground objects. Moreover, separate moving objects can be connected through shadow, which may lead to an inaccurate object detection and localization, and confuse object classification and traffic scene analysis. Therefore, an effective cast shadow detection method is necessary for accurate foreground segmentation in intelligent transportation visual surveillance systems. During the past decade years, many methods of moving cast shadow detection have been proposed [1 13]. A comprehensive study of moving cast shadow detection approaches can be found in the Refs.[14, 15]. Generally speaking, previous works on moving cast shadow detection can be classified into two categories: spectrumbased methods and spatial features-based methods. The spectrum-based approaches employ spectral information including the chromaticity or intensity of regions to detect shadows [1 3,7 9]. A direct way for cast shadows detection is based on the assumption that shadow pixels should have lower luminance and similar chromaticity values as the corresponding background pixels. In Ref.[1], brightness ratio and chromaticity difference were defined, and then each pixel was separated as shaded background or shadow if it had similar chromaticity but lower brightness compared to the corresponding background pixel. The spectrum-based approaches are simple to implement and compute. But they often fail to deal with the scene in which moving objects have darker intensity than the shadow regions, or the scene especially with dark sun cast shadows since hue computation can become inaccurate [12]. Moreover, spectrum-based approaches often require explicit tuning of a large set of parameters repeatedly for each new scene. Hence, they cannot handle the real, complex condition with varying illumination. In order to adapt to the varying environment, statistical learning-based approaches have been recently developed to learn shadow model based on physical properties for moving cast shadows detection or removal [2,3,7 9]. These methods tend to be more accurate than those methods directly using chromacity and intensity information. However, they are still limited to spectral properties, and cannot deal with objects containing the similar chromacity to that of the background. On the other hand, spatial feature-based methods extract the priori geometric information including geometry gradient and textures from the spatial domain to solve shadow detection problems [11,12] Spatial features are less sensitive to lighting changes and present the same textural characteristics in shadow regions as in the corresponding background regions. For example, Leone et al. [12] defined a compact representation of texture based on the coefficients of a Gabor functions decomposition to evaluate the similarity of texture descriptors and photometric properties for moving cast shadow detection. At the same time, the orientation, size and even shape of the shadows can also be used to detect shadows. However, spatial feature-based methods impose scene limitations including specific object types, typically pedestrians or vehicles, requiring objects and shadows to have different orientation, and assuming a unique light source or a flat background surface. Although these two categories have been widely used to detect the moving cast shadow, they are not reliable to discriminate between shadows and objects only relying on the spectral information or spatial features information. Therefore, color properties, brightness, texture or gradient information are combined to detect cast shadows in real and complex scenes [5,16]. For example, Yang et al. [5] exploited the information of color, shading, texture, neighborhood and temporal consistency to detect cast shadows. Unfortunately, these methods are not reliable for the direct use of color information, and do not perform effectively on the combination of these features. In this paper, we aim to present a robust moving cast shadow Manuscript Received Aug. 2012; Accepted Dec This work is supported by the National High Technology Research and Development Program of China (863 Program) (No.2012AA112312), the National Natural Science Foundation of China (No ) and Science and Technology Project of Ministry of Transport of China (No A70).

2 758 Chinese Journal of Electronics 2013 detection algorithm by integrating multiple cues for complex traffic image sequence analysis. We consider the pixel with a lower luminance and greater chromaticity difference as a potential shadow point. Using local texture, global brightness ratio and chromaticity distortion as well as temporal information, three adaptive shadow estimators are designed and cascaded to verify whether a potential shadow point is an actual shadow point in an automatic manner for the various scenes. Experimental results on different scenes show that our proposed method can effectively detect moving cast shadow for different surveillance scenarios. II. Cast Shadow Model and Property According to Phong illumination reflection model, an appearance of a surface depends on its reflectivity and the total energy incident at the surface, the intensity I(x, y) of each pixel p(x, y) is defined as follows [8] : I k (x, y) =E k (x, y)ρ k (x, y)o k (1) where the uperscript k represents red, green, and blue components of color light, ρ k is the surface albedo, O k is the sensitivity of a sensor, and E k can be expressed as a function of the intensity of the light source (i.e. sun light) S p and the ambient light (i.e. sky light) S a: S a k(x, y)+sk p (x, y), E k (x, y) = Sa k (x, y)+w(x, y)sp k (x, y), no shadow penumbera (2) Sa k(x, y), umbra where the weighting factor w(x, y) represents the percentage of the receiving energy when the distant light source is partially occluded (penumbra), the value of w(x, y) ranges from zeros (umbra) to one (no shadow), which means that each pixel of shadow has the same ambient light and the pixels which do not belong to the shadow not only have the ambient light, but also have the same light source. Assuming the object and the background are both Lambertian surfaces, and the same non-shadow region (including background and object) has the same illumination with the light source S p and the ambient light S a. However, every shadow pixel in umbra only has ambient light S a. Simply, the RGB intensity components of the object pixel Io k(x, y), the background pixel Ik b (x, y) andtheumbra pixel Is k (x, y) are defined as: Ib k (x, y) =(Sk p + Sa k )ρ k b (x, y)ok (3) Io k (x, y) =(Sp k + Sa k )ρ k o(x, y)o k (4) Is k (x, y) =Sk a ρk b (x, y)ok (5) If both the light source and the ambient light are white, Sp r = Sp g = Sp b and Sr a = Sg a = Sa b,andis(x, y) of the umbra pixel should be on the line connecting the origin and the I b (x, y) inrgbcolor space as shown in Fig.1. On the contrary, compared with Ib k (x, y), Io k (x, y) of the object pixel is dependent to the albedo of the object, thus I o(x, y) locates at any position in RGB color space. To discriminate the shadow pixel and the object pixel, we define the chromaticity distortion CD(x, y) as follows [8] : CD k = Ik s I Ik b, k = r, g, b (6) s I b Obviously, when both the light source and the ambient light are white, CD k equals to zero. However, in a real indoor or outdoor environment, neither the light source nor the ambient light may necessarily be white. In this case, I s(x, y) will not be on the line which passes through the origin and I b (x, y). But I s(x, y) islocatednear the line and the location will be the same as Îs(x, y). Therefore, for a shadow pixel, CD k (x, y) is not zero; but it obeys Gaussian distribution with the mean m k CD (mk CD 0) and the standard deviation σcd k. Fig. 1. The intensity of shadow pixel, background pixel and object pixel in RGB color space In addition, the brightness ratio between I s(x, y) andi b (x, y) can be defined as: α(x, y) = Is Is I s, I b cos θ = I b I b I s I b = Is, I b I b 2 (7) where, is inner product operator, is the norm of a vector, and θ is a direction angle between vector I s(x, y) andi b (x, y). α(x, y) represents the luminance reduction. Thus, a pixel may be considered as a potential shadow if it satisfies: α min α(x, y) α max, 0 <α min <α max < 1 (8) Moreover, texture is less sensitive to lighting changes and can be used to detect shadow. We define the normalized texture value the pixel p(x, y) as follows: T k (x, y) = 0 i 2 +j 2 1,i,j Z I k (x + i, y + j) I k (x, y) In fact, four neighboring pixels in umbra or background regions often have same or similar illumination E k, thus the normalized texture value of the pixel p(x, y) can described as: T k (x, y) = 0 i 2 +j 2 1 i,j Z 0 i 2 +j 2 1 i,j Z E k (x + i, y + j)ρ k (x + i, y + j)o k E k (x, y)ρ k (x, y)o k ρ k (x + i, y + j) ρ k (x, y) (9) (10) From the above equation, we can conclude that the normalized texture value is dependent on the albedo of objects rather than illumination. Shadow region and background reference surface have the same texture, therefore the normalized texture can be used to discriminate the shadow pixel from the object pixel. We define the normalized texture difference TD(x, y) between shadow pixel and background pixels as follows: TD k (x, y) =Tb k (x, y) T s k (x, y) (11) where Ts k (x, y) is the normalized texture value of pixel p(x, y) inumbra, Tb k (x, y) is the normalized texture value of the corresponding pixel in the background region. Obviously, under the environment of fixed background, TD k (x, y) is zero for each shadow pixel in umbra. In other words, T k (x, y) for each shadow pixel should obey the same Gaussian distribution with the corresponding the background pixel, so it has the same mean m k b,t (x, y) and standard deviation σb,t k (x, y) with the corresponding background pixel. III. The Procedure of This Proposed Algorithm In this section, the frameworks of this proposed method is described in detail, and this approach is based on the assumption that the entire scenario has same light source. Firstly, Gaussian mixture model and a standard background subtraction algorithm are applied to obtain the foreground moving regions of objects. However,

3 Adaptive Moving Cast Shadow Detection by Integrating Multiple Cues 759 this foreground moving mask includes the moving object region as well as the shadow region because they have the same motion. We firstly define a weak shadow detector to detect potential shadow pixels, and then employ the local texture difference estimator with illumination invariance to eliminate the object pixels for the correct estimation of global shadow model. Thirdly, global brightness ratio and chromaticity distortion estimators are used to further eliminate the object pixels. Lastly, a post-processing spatial adjustment is used to verify the detection results. 1. Weak shadow detector As the shadow pixel is always darker than the corresponding background pixel, a pixel which is brighter than the background cannot be a shadow pixel. Defining a pixel which is brighter than the background as a moving object pixel, we can extract it from the set of moving pixels, and place it into the candidate object set. Meanwhile, although the chromaticity value of a potential shadow pixel is not on the line passing through the origin and the corresponding background value, it is located near this line and fall into the conic volume around the corresponding background color. On the contrary, the chromaticity value of an object pixel is located far away from this line and fall out this conic volume. Thus, the direction angle θ between input image pixel and background pixel can be applied to identify whether a pixel is a moving object pixel or a shadow pixel. Therefore, we define the Eqs.(8) and (12) as a weak shadow detector. θ(x, y) θ max or cos(θ(x, y)) cos(θ max) (12) The pixel whose value satisfies this weak shadow detector above can be considered as a potential shadow pixel. By this weak shadow detector, the set of moving pixels are divided into the 1st candidate shadow set S 1 and the candidate object set. However, there still exist some moving object pixels which have been misclassified as the shadow pixels because these moving object pixels are darker than their corresponding background pixels. So extra shadow properties or cues should be considered to verify these misclassified pixels. 2. Local texture difference estimator According to Section II, the normalized texture value T k (x, y) of the shadow pixel obeys the same Gaussian distribution with that of the corresponding background pixel. In order to separate the object pixels from shadow pixels, a learning-based method is used to estimate this mean and standard deviation of background pixel in this paper. Firstly, the normalized texture value Tb k (x, y) foreach background pixel is computed. Then, a single Gaussian model is used to build the normalized texture distribution model for every background pixel, and only those non-moving region pixels are used to learn the normalized texture model. The learning strategy is defined as: m k,t T (σ k,t T =(1 γ)mk,t 1 T + γt k,t b (13) )2 =(1 γ)(σ k,t 1 T ) 2 + γ(t k,t m k,t T )2 (14) where t is time index and γ is a learning rate. We use Maximum likelihood estimation (MLE) to estimate the threshold ϕ k h and ϕk l : ϕ k h (x, y) =mk T (x, y)+3σk T (x, y) (15) ϕ k l (x, y) =mk T (x, y) 3σk T (x, y) (16) with the reliability of 99.73%, P ( 3 < Z < 3) = , Z = N(0, 1). By verifying whether or not the texture difference between a pixel in input frame and background is in the feasible range, each pixel p(x, y) in the candidate shadow set S 1 is determined by { p(x, y) S2, if ϕ k l <Tk (x, y) <ϕ k h (17) p(x, y) O, otherwise where S 2 is the candidate set of shadow pixels and O is the candidate set of object pixels using local texture difference estimator. However, it should be noted that the two neighboring pixels in penumbra have different illumination, thus the Eq.(10) cannot be deduced from the Eq.(9). Therefore, it is necessary to detect the penumbra pixels before the normalized texture value is used to discriminate the shadow. According to the property of penumbra, these penumbra pixels always locate at the edge areas of the moving foreground objects. Furthermore, these penumbra pixels have higher brightness than the umbra pixels, but lower brightness than the background region pixels. We define the penumbra detector as follow: p(x, y) PE, if : p(x, y) FE TD k (x, y) >ThT k (18) Th α α(x, y) < 1 p(x, y) PE, otherwise where FE and PE is denoted as the moving foreground edge region and the penumbra area, respectively, Th T k and Th α are the thresholds for the normalized text difference value in RGB component and brightness ratio, and set to 0.5 x y TDk (x, y) and m α, respectively. 3. Global chromaticity distortion and brightness ratio estimators The brightness ratio and chromaticity distortion have been used to indicate the dissimilarity between shadow pixel and background pixel in earlier studies. Choi et al. [8] used a statistics learning method to estimate the brightness and chromaticity distortion using these potential shadow pixels. However, this method only estimated these distortions on one image and easily subject to the external disturbance including image noise, etc. In order to increase the accuracy and stability of the brightness and chromaticity distortion estimation, we integrate the temporal information of the brightness ratio and chromaticity distortion for the cast shadow detection. For the candidate shadow set S 2 in each frame, we calculate the α and CD k, then build four global histograms h α and h CD k (k = r, g, b) of the brightness ratio value α and the chromaticity distortion value CD k for all potential shadow pixels in this frame. At the same time, we also build four global temporal histograms H α and H CD k of the value α and CD k, and update them for video sequence using the following equation: H t α =(1 γ)ht 1 α + γh t α (19) H t CD k =(1 γ)h t 1 CD + k γht CD k (20) where t is time index and γ is a learning rate. The chromaticity distortion CD k obeys Gaussian distribution, and H CD k also obeys the Gaussian distribution. Similarly, the brightness ratio α and H α obey the Gaussian distribution. We use the following methods to estimate the mean and standard deviation. m k CD = arg max(h CD k (i)) (21) 1 i N (σcd k 1 N )2 = N [(i m k CD )2 H CD k (i)] H i=1 CD k (i) i=1 (22) m α = arg 1 i N max(h α(i)) (23) σ α = N 1 N [(i m α) 2 H α(i)] (24) Hα(i) i=1 i=1 where m k CD, σk CD is the mean and the standard deviation of the chromaticity distortion, and m α and σ α is the mean and the standard deviation of the brightness ratio. i and N is the bin index and bin number of the global temporal histogram, respectively. Thus, we use the MLE to estimate the threshold as follow: α k h = mk CD +1.96σk CD (25) α k l = mk CD 1.96σk CD (26) βb k = mk α +1.96σk α (27)

4 760 Chinese Journal of Electronics 2013 βl k = m k α 1.96σk α (28) with the reliability of 95%, P ( 1.96 < Z < 1.96) = 0.95, Z = N(0, 1). After calculating α k h,αk l,βk h and βk l, by verifying whether or not the α of a pixel is in the feasible range, every pixel p(x, y) inthe candidate shadow set S 2 is determined by { p(x, y) S3, if βl k <α<βh k αk l <CDk <α k h (29) p(x, y) O, otherwise where S 3 is the candidate set of shadow pixels using global shadow estimators. 4. Spatial adjustment Through these above procedures, we can exactly classify the shadow pixels from the object pixels. But it is still inevitable that some shadow pixels may be misclassified as moving objects, or some objects pixels are misclassified as shadows. So a post-processing spatial adjustment analysis is used to confirm the true objects as well as the true shadows according to their geometric properties. The spatial adjustment is described as follows: Step 1 Correct shadow detection failure: In the process of detecting cast shadows, the true shadows sometimes break into isolated shadow blobs. This problem can be easily corrected by morphological close operation. Step 2 Correct object detection failure. If one part of the detected object is misclassified as a shadow, most of the exterior pixels adjacent to the boundary of this region will be located inside the candidate foregrounds. Given this condition, boundary information can be used to confirm whether the shadow candidate is a true shadow or not. In this paper, we adopt the same method as Ref.[17] to solve the misclassified problems. IV. Experimental Results In order to verify the effectiveness of our proposed algorithm, we first qualitatively demonstrate the robustness of our method on different scenes video sequence. Then, we will compare our method with some other methods. Our algorithm was implemented in C++ and runs real-time under Windows XP. For resolution color images, it runs more than 10f/s on a PC with an Intel Pentium dual-core 3.2GHz CPU. The parameters α, β are initialized with 0 and 1, respectively, and learning rate γ is set to 0.3, the bin number of histogram N issetto32inallexperiments. A wide range of scenes with variation in the type and size of objects and shadows are used to test the effectiveness of our proposed algorithm. The sequence shown in the first row of Fig.2 is from the real traffic scene, the other six sequences were introduced in Refs.[8, 15] and have been widely used for testing the shadow detection performance. 1. Qualitative results In Fig.2, we show sample cast shadow detection results from six video sequences. The first three sequences are from the outdoor environments and the last three sequences are from the indoor environments. Fig.2(a) shows one frame selected from the video, where cast shadows are present in the scene. The moving region mask is presented in Fig.2(b), in which the white region indicates the moving region and the black region belongs to background. We show the cast shadow detection results without spatial adjustment and with spatial adjustment in Fig.2(c) and(d) respectively. We can see that in these video sequences, this proposed algorithm can detect cast shadows with a few misclassifying foreground as shadows. 2. Quantitative results In order to further analyze this proposed method objectively and quantitatively, we use two quantitative evaluation metrics proposed by Prati et al. [14], namely shadow detection rate (η) and shadow discrimination rate (ξ) which are defined as follows: TP s TP F η =, ξ = (30) TP s + FN s TP F + FN F where TP and FN denote true positive and false negative pixels with respect to either shadow (S) or foreground objects (F). TP F is the number of ground-truth points of the foreground objects minus the number of points detected as shadows but belonging to foreground objects. A comparison between our proposed method and other methods (including these methods described by Sanin et al. [15] and Choi et al.) on six video sequences is made and shown in Table 1. This comparison shows that the previous approaches exhibit a significant trade-off between the shadow detection and discrimination rates (e.g. high detection rate at the expense of a reduced discrimination rate). Our proposed method, on the other hand, is able to achieve both high detection and discrimination rates with only a minor increase in the average amount of time required for processing each frame. This comparison shows that our proposed technique surpasses the performance of other existing methods. V. Conclusion This paper has presented a robust moving cast shadow detection algorithm integrating multiple cues for traffic surveillance scenarios. A weak shadow detector is firstly used to select potential shadow pixels, and then three shadow estimators based on texture difference, chromaticity distortion and brightness ratio are defined and cascaded to refine the results from coarse to fine, moreover, these shadow estimators can determine these thresholds for shadow detection in automatic manner and without manual setting or an addition training step. At last, spatial adjustment is employed to further verify the detection results. Qualitative and quantitative evaluation validated that this proposed approach is more effective in describing background surface variation under cast shadows than other existing methods. To further improve the detection accuracy, more discriminative features or the spatial constraints can also be incorporated into the detection process in the future. References [1] C. Grana, M. Piccardi, A. Prati, Detecting moving objects, ghosts and shadows in video stream, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, No.10, pp , [2] S. Nadimi, B. Bhanu, Physical models for moving shadow and object detection in video, IEEE Transactions on Pattern Ana- Table 1. Comparison of this proposed method with different cast shadow methods Highway 1 Highway 3 Laboratory Intelligent room Campus Hallway η (%) ξ (%) η (%) ξ (%) η (%) ξ (%) η (%) ξ (%) η (%) ξ (%) η (%) ξ (%) Chromacity [1] Geometry [18] Physical [7] SR textures [12] LR textures [16] Choi et al. [8] Our method

5 Adaptive Moving Cast Shadow Detection by Integrating Multiple Cues 761 Fig. 2. Sample results of detecting cast shadows in various environment. (a) Frame from video sequence; (b) Moving region mask after background subtraction; (c) Shadow detection results without spatial adjustment; (d) Shadow detection results with spatial adjustment lysis and Machine Intelligence, Vol.26, No.8, pp , [3] N. Martel-Brisson, A. Zaccarin, Learning and removing cast shadows through a multidistribution approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.29, No.7, pp , [4] A.J. Joshi, N.P. Papanikolopoulos, Learning to detect moving shadows in dynamic environments, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.30, No.11, pp , [5] M.T. Yang, K.H. Lo, C.C. Chiang et al., Moving cast shadow detection by exploiting multiple cues, IET Image Processing, Vol.2, No.2, pp , [6] Y. Wang, Real-time moving vehicle detection with cast shadow removal in video based on conditional random field, IEEE Transactions on Circuits and Systems for Video Technology, Vol.19, No.3, pp , [7] J. Huang, C. Chen, Moving cast shadow detection using physics-based features, Proc. of IEEE Conference on Computer Vision and Pattern Recogntion, Miami, Florida, USA, pp , [8] J. Choi, Y.J. Yoo, J.Y. Choi, Adaptive shadow estimator for removing shadow of moving object, Computer Vision and Image Understanding, No.114, pp , 2010.

6 762 Chinese Journal of Electronics 2013 [9] L. Zhou, H. Kaiqi, T. Tieniu, Cast shadow removal in a hierarchical manner using MRF, IEEE Transactions on Circuits and Systems for Video Technology, Vol.22, No.1, pp.56 66, [10] A. Amato, M.G. Mozerov, A.D. Bagdanov et al., Accurate moving cast shadow suppression based on local color constancy detection, IEEE Transaction on Image Processing, Vol.20, No.10, pp , [11] W. Zhang, X.Z. Fang, X.K. Yang et al., Moving cast shadows detection using ratio edge, IEEE Transactions on Multimedia, Vol.6, No.9, pp , [12] A. Leone, C. Distante, Shadow detection for moving objects based on texture analysis, Pattern Recognition, No.40, pp , [13] G. Yepeng, G. Weikang, Automatic and robust shadow segmentation from two-dimensional scenes, Acta Electronica Sinica, Vol.34, No.4, pp , (in Chinese) [14] A. Prati, I. Mikic, M.M. Trivedi et al., Detecting moving shadows: Algorithms and evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, No.7, pp , [15] A. Sanin, C. Sanderson, B.C. Lovell, Shadow detection: A survey and comparative evaluation of recent methods, Pattern Recognition, No.45, pp , [16] A. Sanin, C. Sanderson, B.C. Lovell, Improved shadow removal for robust person tracking in surveillance scenarios, Proc. of 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, pp , [17] B. Sun, S.T. Li, Moving cast shadow detection of vehicle using combined color models, Proc. of Chinese Conference on Pattern Recognition (CCPR), Chongqing, China, pp.1 5, [18] J. Hsieh, W. Hu, C. Chang et al., Shadow elimination for effective moving object detection by Gaussian shadow modeling, Image and Vision Computing, Vol.21, No.6, pp , LING Zhigang was born in Yueyang, China, in He received the Ph.D. degree from the Northwestern Polytechnical University, Xi an, China, in Now he is an assistant professor of Hunan University, Changsha, China. His research interests include computer vision and pattern recognition. ( zgling hunan@126.com). LU Xiao was born in Changsha, she received the B.S. degree from Hunan University and M.S. degree from Southeast University. She is now a doctor candidate of Hunan University. Her research interests include machine vision and pattern recognition. ( xlu hnu@163.com). WANG Yaonan was born in He received the Ph.D. degree from Hunan University in He was a postdoctoral researcher at National University of Defense Technology in 1995 and an Alexander von Humboldt Stiftung in Now, he is a professor at the College of Electrical and Information Engineering, Hunan University. His research interests include intelligent control, image processing and intelligent robotics. HE Xi was born in Changsha, China. She will receive the B.S. degree in electrical engineering from Hunan University. Her research interests include signal and image processing. ( hersy2013@gmail.com).