Study of an active contour model: application in real time tracking.

Transcription

1 Study of an active contour model: application in real time tracking. Ouardia CHILALI, Samir HACHOUR, Idir FEDDAG and Youcef IABADENE. Département Automatique, F.G.E.I., U.M.M.T.O., Tizi-Ouzou, Algérie. Abstract The tracking of object moving is problems of topicality in the field of the artificial vision. The method that we presented car the property of local segmentation of active contours geodesic, who are based contours, the property of global segmentation of the model of Chan and Vese, which is based contour, and the possibility automatically of handling the change of topology by its implicit implementation. The computing time is tiny room thanks to the new consideration of the function Level-Set and with simplifications of the heavy terms for calculation. Satisfactory results are obtained after application of the algorithm. Keywords Active contour, reel time tracking, level set, segmentation. I. INTRODUCTION In the video tracking [1], an object can be of nature unspecified. All the scenarios are possible to imagine: a boat on the open sea, a player in a stage with his balloon, a vehicle in motorway, a plane in the sky, a work piece in production lines, etc. According to the nature of the object and the objective of the application to be realized, various representations of the object form (points [2], geometrical forms of bases [3, 4, and 5], articulated model, silhouette, etc.) and of appearance (templates, the active appearance model (AAM), etc.) are used allowing, thus, several algorithms of tracking. All the algorithms of objects tracking require a mechanism of movement detection, that it is on all the images of the scene or only when the object appears. In the absence of the movement of camera, the objects moving represent the principal source of difference between the images. On the basis of this report, the current segmentation of an image consists in defining the bottom of the image like the static part of the image and the objects like the moving part of the image. This segmentation is thus a detection of movement. The work of Wren and al. (1996) [6], goes on the basic subtraction estimated. For recognizing the changes during time, they proposed to model the color of each pixel. Then, of the parameters, such as the average and covariance, will be the subject of information on the color of several successive images of the sequence. While being based on this information, the bottom of the sequence is considered and modeled. Thus, to detect a movement, it would be enough to make a direct subtraction between the various images of the sequence and this estimated bottom. Other approaches use, in addition to information on the color of the pixels, of the information based on the areas of the image. For example, Elgammal et al. (2000) [7] used a not-parametric estimate to model the bottom of the sequence. During the process of subtraction, the pixel running is compared, not only with the pixel which corresponds to him in the content, but also with the pixels which are close for him. What gave to this method the capacity to compensate for the small movements of the camera. In 2002, Li and Lueng [8] used blocks of pixels 5X5, having information on the color and texture, to model the bottom of the sequence. The use of information on texture made this method much less sensitive to the changes of brightness. Rittscher et al. (2000) [9], used the model hidden of Markov(HMM), to represent the variation of the intensity of a pixel like a discrete state compared to the events of its environment. Another method, based on the modeling of the individual variation of the pixels, is developed by Oliver and al. (2000) [10]. Among the many already existing effective methods, the approaches by active contours [11] seem a promising technique in object tracking. However, the question which arises, naturally, is: «Which type of active contours to choose and adopt for an application in real time object tracking?». Actually, the simplest answer is to choose active contours which are based on the explicit implementation, but with the detriment of much of disadvantages such as the known problem of initialization and the no-authorization of the change of topology. So, implicit active contours (implementation by levels sets) are more advantageous. However, if the implementation by levels sets offered convincing results, it shows the possibility for some particular weaknesses to authorize a real time tracking. The objective of this article is to adopt an implicit active contour model for an application to real time tracking. In section 2, recalls on active contours are presented. The third section was deployed to detail the adopted model which is, theoretically, rather good for an application in real time. In the same section we will describe the implementation of the model while passing by its application on a static image, to come to its application for the real time object tracking. In the

2 fourth section, we spread out the results obtained for various tests. II. RECALL ON ACTIVE CONTOURS The algorithms used for the objects tracking are similar to those of the static segmentation by active contour, except that here the process of segmentation is applied to each image of the sequence, all one imposing that the initial contour of each image is contour finale of the image which precedes it. The principle of active contour is to evolve it towards the edges of the object be followed. This evolution is controlled by the minimization of an energy functional, which is formed by descriptors (based contour, or based region) characterizing the interesting object. Generally, this energy takes the following form [12]: With: v the position of a point. E in, internal energy. It often includes a term which handles the curve of the contour, and which handles its regularity. E context includes the additional constraints. E im which is seen like the potential energy of contour, it contains is local descriptors terms such as for example the gradient in the neighborhoods of the object from where the nomination based edge, or is global terms like information on the color or texture in the internal and external regions with contour, from where the nomination based region [13]. The major restriction in based edge methods is that contour must be initialized not very far from the object, so that it is detected. In the based region methods, this restriction can be removed, and contour can be initialized: that is to say inside, outside the object or at any other place. Recently, the energy based on based-region terms and based-edge terms are very answered in the algorithms of objects tracking by active contour. Paragios and Deriche (2002) [14], used the gradient of the image and the based-region terms to formulate energy: E image = λe contour + (1 λ) E region. (2) where λ is a positive constant. In addition to initialization and energy functional to be minimized, research was also based on the optimal representation of active contour. Thus, a contour can be represented explicitly (curve planes), or implicitly (Level set). In an explicit representation the relations between the various points of contour and of the image are represented by equations. On the other hand, in an implicit representation, the image is seen like a grid, where the distances from all the points of the grid compared to contour are encode positively or negatively, according to their position compared to contour. The evolution of contour is done by the change of the values of the grid evaluated in each point, and will be controlled by a energy functional to minimize. The most significant contribution of the implicit representation is that it is flexible (1) with the changes of topology (division and merging of contour). III. ADOPTED MODEL Our goal is to find a method rather fast, having at the same time, and the property of local and global segmentation. For that, the adopted model [15] is based on the model of active contours geodetic (CAG) [16] which is based-contour, and the model of Chan-Vese (C-V) [17] which is based-region. The model is implemented implicitly by level set. It is to remind, that the evolution equation of the CAG is given in the following form: Γ. I (3) where: g is the stopping function. On the other hand, that of C-V is given as follows: Γ g µ (4) The adopted model combines the advantages of two models CAG and C-V for its construction. Indeed, it uses the statistical data which it calculates at base of and (average with interior and external of contour, respectively) of the equation (4) and exploits the formulation of the equation (3) to lead to a model for implementation by replacing the function g of the equation (3) by another function known as signed pressure force (SPF). The latter is a force generated by the difference of the average of the intensities inside and outside the object (c1 and c2 respectively) and their averages. This force applies a pressure incentive contour to this shrinking or increasing according to its sign. The formula of the SPF is given by the following equation: I, Ω (5) The representation of appearance is made by the average of the intensities of the various regions of the image. In the formula (5), the numerator gives the sign of the SPF, thus to handle the contour evolution direction. The denominator is introduced to give a value to the SPF in [-1,1], i.e. Normalization. Once that the SPF of the equation (5) is calculated, it will be replaced in the equation (3) instead of the function g what enables us to lead to the following formulation: Γ. I. I (6) In more to the introduction of region information into the formula (4), the adopted model does without the following terms from there [15]:

3 Ix. I : It is the term based contour used to attract contour towards the edges of the objects to be detected. What is assured, in a more robust way, with the new force based area SPF. : It is a term of curve used to regularize contour. In the model that one adopted, the function Level-set is initialized as a constant having a sign opposed inside and outside contour, with the result that 1. The term of regularization can be replaced by, which is the Laplacian of. While being based on the theory of (scale-space), one can say that the evolution of the function level-set ( ) with the Laplacian is equivalent to filtering with the Gaussian kernel. While taking as a starting point this idea, the model uses a Gaussian filter, independently of the criterion of evolution, in order to regularize contour, and that, by carrying out a product of convolution after each iteration of the process of convergence. We end to the following adopted model, which will be implemented by the level sets:., v Ω. (7) where: a constant which is added to increase the speed of the evolution. If we want to segment only one desired object (local segmentation), the following conditions must be satisfied: The function must take only two possible values: = +1 for all positive values of or = -1 otherwise. The Contour must be initialized very close to the object to detect. The sign of the SPF must be chooses in a manner that the evolution of contour will made from less intense to most intense, if the object is of less intensity, compared to that of the image, or, of from the less intense to most intense if the object to be segmented is of intensity more significant than that of the image. The adopted model has several advantages compared to those of CAG and C-V, which motivated us to adopt it for our application. The different steps of the algorithm are described as follows: a) Level-set function initialization (): all basic geometrical forms (rectangle (Fig. 1.a), ellipse (Fig. 1.b), etc.) can be taken as contour initial form. This function will take value 1 if the pixel is inside, -1 if it is outside and 0 on contour. a) Initialization by rectangle. b) Initialization by ellipse. Fig. 1 Initialisation examples. b) Calculation of the average intensities inside and outside of contour: it is done same manner as in [17]. c) Evolution of the function level-set: is done according to the equation (7). d) For the regularization of the level-set function, we convoluted it with a filter of centered Gaussian kernel with standard deviation (σ). e) Criterion of convergence: SPF = 0, if not repeat since the stage b. To highlight qualities and the limits of the approach which we adopted, we chose divide our work into two parts. A. Application to the static segmentation The steps which we followed to apply the algorithm of segmentation to a static image are summarized in the flowchart of Fig. 2. Begin Images Acquisition Conversion on the gray level Initialization Evolution Criterion of convergence End yes Fig. 2 Static segmentation flowchart. To highlight the advantages and the limits of the adopted approach, we carried out tests on images containing more or less complex objects to segment. We represent two results (Fig. 3) which highlight the property of local and global segmentation of the approach that we adopted. This property is explicit before. The object segmented in the Fig. 4 is known as complex, with no regular edges. We see well, that with a local segmentation the other objects are ignored and the object of interest is very well detected, after 18 iterations and an execution time of 1,152secondes. The experiment of Fig. 5 highlights the faculty of the method for detection of the objects having no apparent edges. Convergence is achieved after 27 iterations in 1,635 seconds. no

4 a) Local segmentation. b) Global segmentation. Fig. 3 Local et global segmentation. Fig. 4 Circle initialization, local segmentation, α =25. Fig. 5 Ellipse initialization, local segmentation, α=25. The adopted method has certain robustness with regard to the noises. The detection of the narrow zone of the object highlights the precision of the contour, which can be modified by the Sigma parameter of the Gaussian filter, as we can see it in Fig. 6. The object is detected after 0,26 seconds, at the end of 29 iterations. Figure 6. Rectangle initialization, α =25. =5. B. Application to the object tracking In order to carry out our application on object tracking, we proceeded by the choice of a method of moving object detection, then the application of the algorithm of segmentation for tracking. The different steps of the process are described as follows: Background estimation: In our case, the background is regarded as the image in absence of an unspecified object. It is an image taken using a fixed camera. Acquisition of the first image of the sequence: This image is taken with the appearance of the object to follow. Detection of the object moving: Being given that the camera of the experiment is fixed (fixed background) and that the tracking wants to be an application in real time, we chose a movement detection method based on the subtraction of the successive images of the sequence with the background. The movement detection method that we adopted bases on the following steps : Subtraction: the algorithm carries out a direct subtraction, pixel with pixel, between the image of the sequence and the background. Image processing resulting from the subtraction: In order to make a success of the tracking well, a treatment is carried out to distinguish the different regions from the resulting image (contrast increasing and binarisation). Application of the step a on the resulting image. Application of the steps b, c, d and e. Definition of final contour like initial contour of the subtraction result of the following image with the background. Repeat the two last instructions until the end of the sequence. IV. EXPERIMENTATION For our experimentation we used as material a Portable PC (Acer 5100 processor AMD Mono-Core (only one core) 1,7GHz. A RAM of 1Go. Webcam (Acer Orbicam) integrated) and external Webcam (D-Tech USB (digital technology). Model FM806. Resolution 1,3 Mega pixel, max 1280x960. Video Formats RGB24 and I420). Our application is developed under Matlab 7.8. A. Application on a video recorded The video used, for this test, is named «vipmen.avi» of Matlab. 1) Direct Application: Our directly applied our algorithm such as we announced it before. The result is illustrated in Fig. 7. The image (1) is used as background owing to the fact that it does not contain an object moving. For (2, 3 and 4), the first person was detected and followed by contour in green. The second person (in 5, 6, 7 and 8) also was detected and followed successfully. However, one notes defects of segmentation which are explained by: change of intensity (it is due to the entry of the two persons in the scene. That can be seen into 5, 8 and 12. The two silhouettes are partially segmented), complex background (the white shirt was not well detected owing to the fact that it has the same intensities as the background. Also, black trousers with black earthenware) and sudden movement (illustrated by image 9, where red contour means that convergence is not reached yet). 2) Defects correction: A simple additional treatment on the image resulting from the subtraction (correction by mathematical morphology (Dilation)), us made it possible to obtain better results (Fig. 8). We note a clear improvement compared to the first two defects of persons segmentation of

5 the preceding test. However, we note also the persistence of the defect related to the sudden movement. B. Application of tracking of a hand moving In what will follow, we would have the results obtained during the experiments made for the hand fingers movement tracking in real time. Thus, the images of (1) to (6) of Fig. 9 are samples taken of a sequence acquired in real time. The segmentation is enough robust to handle the changes of intensity within the object, as we can note it in the images (1), (2) and (3). For the image (7) the inch is badly detected, which is due to the significant change of intensity at its end. The fingers are quickly segmented dice their appearances. During the evolution of contour, we noticed that contour takes another color (red) when there is a fast movement, which means that convergence is not reached.. C. Application of specific rigid object tracking with criterion of size We stated previously that to carry out a local segmentation, contour must be initialized very close to the wanted object, which is not easy to realize in a video. The following experiment was freed some by using a priori additional information on the object to be followed. Indeed, a modification was made to the level of our algorithm, just after the step of subtraction of each image acquired with the image of reference. The goal of this modification being to affect a color for each object moving, to count the number of objects moving and to calculate their surfaces. We choose an object according to his size. Moreover, to evaluate the made modification, we could make a small experiment. Indeed, we conceived with the assistance: of a metal part board box, in the form of "E" and of "I" and a magnet, environment of adequate work. Using the magnet placed of the other with dimensions of the paperboard, we make move the two metal parts which are other with dimensions, according to the desired trajectory. The results obtained are illustrated by Fig. 10 and Fig. 11. V. CONCLUSION Main goal of this article was to apply a model of active contour for real time objects tracking. The selected model is implemented with level-set, and combines the advantages of based -region segmentation method (C-V), and another basedcontour (CAG). The principal contribution of the adopted approach lies in the computing time, which are considerably tiny room thanks to the new consideration of the initialization of the function level-set and the restrictions brought to the functional calculus which one is judicious to minimize for convergence. Relative with the material that we used, we estimate to have very good results, except that, despite everything the efforts provided for better accelerating the algorithm, there remains unable to follow the objects having a movement at relatively high speed. Let us note however, that the localization of the objects moving in the scene observed is forced several parameters such as the speed of displacements, the quality of the images of the sequence (variations of lighting, background not uniform, etc.). Thus, in order to cure these limits, and to widen the field of application of this method, of the prospects are possible, such as, the estimate of the background of the sequence which could be useful for the object tracking with a mobile camera, the use of several functions Level-set in order to be able to detect more than only one difference in intensity in the image (segmentation of several different objects). REFERENCES [1] A. Yilmaz, X. Li and M. 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6 Fig.7. Direct application on a video Fig. 8 Correction of the defects with the application of a dilation with structuring element with 6-connexity.

7 (1) (2) (3) (4) (5) (6) (7) Fig. 9 Real time tracking of the hand Fig. 10 Largest object tracking Fig. 11 Smallest object tracking.