IJMTES International Journal of Modern Trends in Engineering and Science ISSN:

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1 A Novel Method to Count the Number of Cars in an Unmanned Aerial Vehicle Images A.Mathavaraja 1, R.Sathyamoorthy 1 (Department of ECE, UG Student, IFET college of engineering, Villupuram, Tamilnadu,mathavarajaifet@gmail.com) (Department of ECE, Assistant Professor,IFET college of Engineering, Villupuram, Tamilnadu,vrsathyamoorthy@gmail.com) Abstract In this paper, a novel method is proposed to detect the number of cars in an UAV images. The primary step for car counting starts with screening, which separates the interested area in the images. The primary step followed by key points extraction by using an SIFT algorithm, which are highly distinctive. In addition to that, for suppressing the background in images, Foreground detection method is proposed, which will exhibits a promising car counting accuracy. Finally key points can be grouped by using an merging algorithm. The promising results indicate the effectiveness of our proposed framework, with higher accuracy. Keywords Unmanned aerial vehicle (UAV), Support Vector Machine(SVM), Scale Invariant Feature Transform (SIFT), Car Keypoint Merging. 1. INTRODUCTION In the past few years, many studies have focused on the improvement in the good level of life quality, in which particularly made attempt to the transport system. The transport planner in developing countries gives importance to the increase in the speed of the transport system, environmental pollution, utilization of resources in Urban Transport system. Since, they face the major problems due to increase in population, growth in industrial sectors, and the fragility of public transportation system. The growth of traffic in urban areas is highly complex to control the transport system. In fact, the intelligent transportation system gives more attention to prophesy and prevent the traffic jam congestions, accidents to bring down into the smaller extent, to restrict the environmental impact and to improve the traffic safety. Being monitoring the vehicles in urban areas will help in the surveillance application such that it could be faced by counting the number of cars in the specific parking area and along the roads. Unmanned aerial vehicles (UAVs) are aircrafts without human pilots on-board, which is commonly known as Drone. Pilotless aircraft have been designed from early in the development of flight, but despite their complexity they have been greatly expanded to the various applications such as military, agriculture, geology, forestry, regional planning, surveillance and education because of their capabilities. Electric, Ecologic, silent, safe, flexible and customizable are the various interesting characteristics capable of provides an aerial photography which it can be fitted with various imaging sensors(i.e., multispectral /hyper spectral imaging systems) in UAV s. The main advantage of UAV is to acquiring information immediately without affecting the life of people. Hence, UAV s can utilize for the work of object detection and tracking problem as depend on the desired applications. key points can be done by using the SIFT transforms. Key point classification is to discriminate between background versus car hence the number of key points can be obtained by using an merging process. [1] Far id at el. Proposed a car counting method by using an Histogram of gradient techniques instead of SIFT transform.[] Scale invariant feature transforms(sift) by David Lowe presented by Jason Clemons gives an clarity knowledge about the matching problem and Feature Characteristics such as Scale Invariance, Rotation Invariance, Illumination invariance, Viewpoint invariance.[3] Fig1 Proposed method block diagram. LITERAUTRE SURVEY Thomasatel. Proposed a method the car detection and counting problems in an UAV images by starting with screening steps where to reduce the false alarm. Extraction of Fig illustrates the screening process Volume: 03 Issue:

2 3. SCREENING Assuming that usually cars in urban scenarios lie only over asphalted areas (i.e., roads or parking lots), we will restrict the investigated areas only to these regions. This provides us two significant advantages: 1) To improve the velocity of detection by limiting the areas to analyze ) To reduce the number of false alarms. The recognition of roads and parking lots can be envisioned in two manners. In the first one, the most accurate one, the mask to isolate the regions covered by asphalt is obtained from road maps possibly available in a Geographic Information System (GIS) covering the areas under analysis. In this way, no new screening is required since all asphalted areas are known a priori, making it easy to build the desired mask. 4. BACKGROUND SUBTRACTION Background subtraction, also known as Foreground Detection, is a technique in the fields of image processing and computer vision wherein an image s foreground is extracted for further processing (object recognition etc.). Generally an image s regions of interest are objects (humans, cars, text etc.) in its foreground. Background subtraction is a widely used approach for detecting moving objects in videos from static camera. In this method the original background image[fig 3.A] is consider as reference image and the present image[fig 3.B] is subtracted from the current image. Hence such that the subtracted image is obtained in the figure 4. fig 3.a and 3.b illustrates the original image and masked image 5. KEYPOINT EXTRACTION In the previous step, we limited to only the specific roads or parking areas covered by the regions is known as asphalt zones.. In this step, the interesting points on the object in an Image can be obtained to provide a feature description of the object. Since, our study is based upon the extraction of features in particular class of objects. The feature obtained in the image must be detectable to scale changes, rotation, translation, noise and illumination. This is because the extracted output image can be used to detect the object when attempt to locate the object in a test images while containing many other objects. Usually the points in the image can be finding on high contrast side, such as object edges. Scale-invariant feature transform[sift],gradient locations and orientation histogram, shape context,spin images, steerable filters and differential invariants are the several object descriptors satisfying the requirements of computer vision Fig 4 demonstrates the subtracted image from the original image Such these descriptors are based on the points of interest in the image. In this context we are going to use the Scaleinvariant feature transform (SIFT), published by David lowe in It is specially proposed to find and describe features in image. The SIFT algorithm is majorly used in identify objects even in partial occlusion in the computer vision system due to its distinctive properties (scale changes, orientation, illumination). Object recognition, individual identification of wild life,3d modelling, and match moving are some of the applications of SIFT transform. Hence, It well suitable for our scope, i.e the detection of cars in an UAV images which is characterized by extremely highly spatial resolution. Lowes method is a good solution to overcome the various obstacles our method in the images. The various problem statements in this method are identification of cars in terms of shape, the colour, and the variable position conditions such as rotation and scale problems. It has the capability to overcome the problems of partial occlusions (i.e. cars partially hidden by trees or shadows) in urban environment. The algorithms is mainly composed of four steps such as 1. Scale-space extrem detection. Key point localization 3. Orientation assignment 4. Generation of key point descriptors. To detect the identification of various possible locations of key points in image, The algorithm begin with the scale space extreme detection, which are invariant to scale changes. This Volume: 03 Issue:

3 is because to search the stable points across various possible scales in the images. I(x,y) is the given input images is convolving with Gaussian filter G(x,y) to form a Laplace of Gaussian Lxyσ (,, ) = I(x, y)*g(x, y, σ) 1 x + y LXY (,, σ ) = I(x, y)* exp( ) πσ σ Where Gaussian filter can be expressed in the form of 1 x + y Gxyσ (,, ) = exp( ) πσ σ Where * is the convolution operator for convolving the Gaussian filter and input image. σ is the scale factor,such the resultant image shows the difference-of-gaussian filter. The stable location for keypoint can be done by scale-space extrem a detection by using the difference of Gaussian function, which is expressed as GxyK (,, σ) Gxy (,, σ) Dxy (,, σ) = LxyK (,, σ) Lxy (,, σ) The scale σ and kσ is the difference between the Gaussian blurred images. k is the constant multiplicative factor which separates the images at different scale. In order to find the keypoints of various possible locations in the image, local maxima or minima of the DOG across different scale should be identified. Each pixel in the DoG images is compared to its 8 neighbors at the same scale, plus the 18 corresponding neighbours at neighbouring scales hence totally it is compared across 6 neighbours. If the pixel is a local maximum it is selected as a candidate keypoint. The difference of Gaussian function obtained in the images is more delicate to noise and poorly localized along edges. Eliminating the points with low contrast and noise in the image is mandatory. This can be upgrade using second order Taylor series expansion of the scale space function. For each and every keypoint interpolation of nearby data is used to accurately determine its position. T D 1 T D DX ( ) = D+ X+ X X X X D is the derivative calculated at the sample point and x=(x,y,σ)t is offset from this point. Derivative of previous equation with respect tox and setting it to zero, we get 1 D D X = X X In order get the location of keypoints in terms of (x,y,σ). If X>0.8, then it means the maximum lies closer to a different sample point. Substitute the previous equations we obtain hence so we can easily discard them. T 1 D DX ( ) = D+ X X The location of D(X) smaller than the value of 0.8 are eliminated. The DOG produces a response only along the edges, but the location of the keypoints across the edges are weekly determined and could be unstable even with minute amount of noise. Fixing a threshold value to eliminate the peak values keypoint is important. The DOG has a principle of curvature across the edge and the small curvature in perpendicular direction. Hessian matrix is calculated from principal of curvature in x matrix. This is mainly used for edge response elimination. Dxx Dxy H = Dyx D yy As the Eigen values [H] are proportional to principal of curvature [D], keypoints with low contrast are discarded. Let α be the Eigen values with the largest magnitude and β be the smallest one. So that we calculate the sum and product of the Eigen values from the trace and determinant of H which is calculated as follows Tr( H ) = Dxx + Dyy = α + β Det( H ) = DxxDyy ( Dxy ) = αβ Let r be the ratio between the largest Eigen value and the smallest one, which is expressed as Tr( H ) ( α + β) ( r + 1) = = Det( H ) αβ r In order to check the ratio of principal curvatures is below some threshold, we need to check whether Tr( H ) (r+ 1) < Det( H ) r Although a set of invariant points is now calculated from the previous step, It should fulfill the requirement of the characteristic features (locations invariants to rotation, scale changes, illumination). In this context, a key point is assigned in one or more directions based on the given image. Thus this step is heart of this algorithm which helps to satisfy invariance to rotations. At the given blurred image L(x,y,σ) at this scale and magnitude m(x,y). and orientation ϴ(x,y) are calculated using the pixel difference. mxy (, ) = ( Lx ( + 1, y) L(x 1, y)) + ( Lx (, y+ 1) Lx (, y 1)) 1 ( Lxy (, + 1) Lxy (, 1)) θ ( xy, ) = tan ( Lx ( + 1, y ) Lx ( 1, y )) Volume: 03 Issue:

4 The magnitude and direction calculations for the gradient are done for every pixel in a neighboring region around the keypoint in the Gaussian-blurred image L. An orientation histogram with 36 bins is formed, with each bin covering certain degrees. The peaks in this histogram correspond to dominant orientations. Once the histogram is filled, the orientations corresponding to the highest peak and local peaks that are within 80% of the highest peaks are assigned to the keypoint. In the case of multiple orientations being assigned, an additional keypoint is created having the same location and scale as the original keypoint for each additional orientation. In the last step we determine the keypoint orientation at its particular scales in the given blurred image. This confirms invariance to image location, scale and rotation. Hence we we want to compute a descriptor vector for each keypoint such that the descriptor is highly distinctive and partially invariant to the remaining variations such as illumination, 3D viewpoint, etc. First a set of orientation histograms is produced on 4x4 pixel neighborhoods with 8 bins each. These histograms are computed from magnitude and orientation values of samples in a 16 x 16 region around the keypoint such that each histogram contains samples from a 4 x 4 subregion of the original neighborhood region. Since there are 4 x 4 = 16 histograms each with 8 bins the vector has 18 elements. This vector is then normalized to unit length in order to improve invariance to affine changes in illumination. These are the set of N keyoints consider for merging the keypoints in the considered image I(x,y). The main steps of our merging algorithm are summarized. Step 1: The spatial coordinates of the keypoints contained in the set Kc are used as input of the algorithm. Step : To the vector of parameters, a further parameter m is added and initialized to 1. It will act as a counter to keep trace of the number of merging operations done with that keypoint. Step 3: A matrix N N containing the Euclidean distances in the spatial domain between all keypoints is computed. Step 4: The two keypoints (ki, k j ) with the smallest distance dmin are selected. Step 5: If dmin < Tm (threshold) ki and k j are merged into a new point kt which will replace the two keypoints in the set Kc. Step 6: The matrix containing the distances is then recomputed with the new point. Steps 3 6 are repeated until dmin > Tm. Step 7: Assuming that the points with a value of m smaller than are isolated points only the points with m > 1 are kept. The number of resulting merged keypoints represents finally the estimation of the cars present in the image. This step is useful to detect the isolated keypoint and discard them since Fig 5 illustrates the keypoint extraction obtained from SIFT algorithm. 6. KEYPOINT MERGING The objective of this method is to find the number of cars in the given image I(x,y) by using the keypoint. Whereareas the keypoint obtained from the scale-invariant feautre transform(sift). It is possible that a single car may contain more number of keypoint. In order to improve the accuracy of the merging method the following procedure are maintained. Let Kc be defined as K c ={K 1, K,K 3.K n } Fig 6 illustrates the keypoint merging method useful to detect isolated keypoints and discard them since viewed as false alarms 7. CONCLUSION This paper presents a solution for the automatic detection and counting of cars present in images collected by means of an UAV. Our work starts with a screening step which help us to focus on the asphalt zones(e.g., roads and parking lots). This method allow us to decrease the areas of investigation making the algorithm faster and with fewer false alarms. The second step is focalized on the Background subtraction which help to detect the cars in the given image. Keypoint extraction is obtained using the SIFT algorithm. In the last Volume: 03 Issue:

5 part of this paper, an algorithm was implemented to merge the car keypoints belonging to the same car. This step is necessary because, at the end of the keypoint classification, a car is typically identified by more than one keypoint. 8. ACKNOWLEDGMENT I take the opportunity to thank Mr.R.Sathyamoorthy,(Asst professor) Department of Electronics and communication engineering for his encouragement, support and untiring cooperation. REFERENCES [1] Thomas Moranduzzo and Farid Melgani, Automatic Car Counting Method for Unmanned Aerial Vehicle Images ieee transactions on geoscience and remote sensing, vol. 5, no. 3, march 014. [] Thomas Moranduzzo, and Farid Melgani, Detecting Cars in UAV Images With a Catalog-Based Approach ieee transactions on geoscience and remote sensing, vol. 5, no. 10, october 014. [3] Scale invariant feature transorms(sift) by David Lowe presented by Jason Clemons gives an clarity knowledge about the matching problem and Feature Characteristics [4] Vehicle Detection and Classification from Satellite Images Based On Gaussian Mixture Model International Journal of Engineering Research and General Science Volume 3, Issue, Part, March- April, 015. [5] Car Detection and Counting Method by using Data Mining / Warehousing 014 International Journal of Advance Research in Computer Science and Management Studies Research Article Volume, Issue 7, July 014. Volume: 03 Issue: