REAL TIME ESTIMATION OF VEHICULAR SPEED USING NORMALIZED CORRELATION

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 12, December 2017, pp , Article ID: IJMET_08_12_065 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed REAL TIME ESTIMATION OF VEHICULAR SPEED USING NORMALIZED CORRELATION Saranya K.C*, Aarthy M, Utthara Thiagarajan, Nicole Barboza and Tharrun Ravishankar School of Electronics Engineering, VIT, Vellore, Tamil Nadu, India ABSTRACT: Monitoring speeds of vehicles on the road is of increasing importance, to avoid rash driving and prevent accidents from taking place. It is an interesting challenge to obtain the speed of vehicles as they swiftly pass on the road. In this study, an image of car is captured from a video in real time. These images are analyzed to find coordinates at different instances using normalized correlation between the input image and the template image of the vehicle. Lastly these coordinates are used to evaluate the centroids to determine the distance travelled and the speed. The study was conducted using footage database of different cars and roads for testing. The results obtained were with an accuracy of 99% when compared to the available CCTV database. Keywords: Centroid, Normalized Correlation, Speed Estimation, and Video Processing Cite this Article: Saranya K.C, Aarthy M, Utthara Thiagarajan, Nicole Barboza and Tharrun Ravishankar, Real Time Estimation of Vehicular Speed using Normalized Correlation, International Journal of Mechanical Engineering and Technology 8(12), 2017, pp INTRODUCTION In current times, it is vital to ascertain the pace of vehicles for law enforcement and implementation of speed limits, the reason for which is the safety of civilians, to avoid accidents and track vehicles. Automated traffic surveillance is an urgent need to expand traffic control and its management. Thus an efficient system of speed estimation of vehicles is vital for numerous applications. Gupta et al. 1, described speed estimation using centroid method, though two template images were required to compensate for the progressive increase in the car s size. Siddharth Jhumat et al. 2, estimated speed of a vehicle using video frames with the help of three techniques namely, normalized cross correlation, frame differencing and optical flow approach. However the results showed inaccuracies when shape of the cars changed. Sen- Ching. S.et al. 3 compared various background subtraction algorithms for detecting vehicles and pedestrians. It was observed that Gaussian filter produced the best response in ideal editor@iaeme.com

2 Real Time Estimation of Vehicular Speed using Normalized Correlation conditions. Ali Taherkhani et al. 4, posed a method for speed estimation of spherical object in frequency domain. This was accomplished by camera parameters and imaging geometry but results were obtained with reference to a stationary background. In this paper, a video of moving vehicles on a road was taken and the template image of the target car was captured manually and converted to HSV color scheme. Normalized correlation was performed between the image and template to find regions of peak correlation for analysis. This was followed by the computation of the centroid leading to the estimation of speed of the vehicle. Section 2 elucidates the methodology followed for the implementation of the speed detecting system using MATLAB software. Results and discussions are presented along with snapshots in Section 3andConclusion is discussed in Section 4. References are encompassed in section MATERIAL AND METHODS: The methodology given in Fig(1) was followed to implement the speed estimating system Image acquisition A video of moving vehicles was taken with the help of a video camera placed at a height to attain the vertical movement of cars at an angle and an image of a vehicle was captured as shown in Fig(2). And the template was acquired manually Fig (3) Image preprocessing For analysis, the image and the template image are converted to HSV color format, refer Fig (4). This is done as an image pre-processing step to separate color components from intensity components for illumination changes. The value component, which results in a 2 dimensional matrix is acquired for accurate analysis Template matching Template image is matched with the image by normalizing both the images. Normalized correlation involves cross correlation in frequency domain which depends on the size of the images. Following which, local sums are calculated to normalize the cross correlation to acquire the correlation coefficients using Eq. (1). This type of correlation is one of the most efficient as it doesn t impose the need for the image and the template to be of the same size. The regions of maximum correlation are then bounded by an outline whose size equals that of the template imageas shown in Fig (5). These steps are repeated for the next instance of the vehicle are presented in Fig (6). ( ), ( ) -, ( ) - *, ( ) -, ( ) - + (1) Eq. (1) characterizes the formula for normalized correlation. frepresents the image of the vehicle whereas is the mean of the template image., Signifies the mean of (,) in the region under the template Centroid values computation The first two coordinates of the matched block are determined by using the x axis and y axis offset values around the region of interest, following which the next two coordinates are obtained by adding the size of the template to the previously stated coordinates. This is repeated for the second instance as well. Values of the centroid for both instances are estimated by taking the average of the two extreme coordinate values of the matched block with respect to the x and y axes independently as illustrated in Eq. (2) editor@iaeme.com

3 Saranya K.C, Aarthy M, Utthara Thiagarajan, Nicole Barboza and Tharrun Ravishankar ( ) ( ) ( ) ( ) (2) 2.5. Estimation of speed Distance in pixels is computed from the centroid values using the distance formula. The corresponding on road distance in meters was then determined. Following this the time was notified from the CCTV camera. The distance and time were then used to estimate speed of the vehicle in meters per second. Lastly, this value was converted to kilometers per hour, and the MATLAB output of the estimated speed is shown in Fig (7). This value was compared to that of the CCTV footage, Fig (8) to determine the accuracy Real Time Case It was then extended to a real time scenario, comprising of a video recorded using a mobile phone camera as shown in Fig (9). Template matching was carried out and a three dimensional shaded plot was generated with pixel values on the x and y axis with versus the correlation coefficient on the z-axis for both instances as illustrated in Fig (10). The regions shaded in red signify the points where the correlation coefficient is between the threshold range of 0.8 to 1. A bounded outline is formed around this region for coordinate estimation. Results obtained are presented in Fig (11) and they exhibited an accuracy of 98%. 3. RESULTS AND DISCUSSION The paper furnished an effective method to determine the speed of a target vehicle with a high degree of accuracy of 99% when compared with the data base. Normalized correlation proved to be a less tedious method as it does not stipulate for the template image to be the same size as the input images. Results were obtained with better precision compared to previous studies. This technique was then employed on real time cases to obtain results with least amount of error, hence proving the system s ability to function in ideal conditions. Table 1 Coordinates of Matched Template of First Instance Coordinate Value Coordinate Value x1(1) 646 y2(1) 246 x2(2) 603 y2(2) 375 x2(3) 749 y2(3) 375 x2(4) 749 y2(4) 246 Table 2 Coordinates of Matched Template of Second Instance Coordinate Value Coordinate Value x1(1) 647 y2(1) 157 x2(2) 647 y2(2) 286 x2(3) 792 y2(3) 286 x2(4) 792 y2(4) 157 Axis Table 3 Centroid Values of First Instance Centroid Value Cx1 719 Cy editor@iaeme.com

4 Real Time Estimation of Vehicular Speed using Normalized Correlation Axis Table 4 Centroid Values of Second Instance Centroid Value Cx2 676 Cy (1) (2) (3) (4) (5) (6) editor@iaeme.com

5 Saranya K.C, Aarthy M, Utthara Thiagarajan, Nicole Barboza and Tharrun Ravishankar (7) (8) Figure (1) Flowchart of the methodology (2) Image of car (3) Template image captured manually (4) Images after preprocessing (5) Outlined image of car after template matching (6) Outlined image of car for next instance (7) Speed of car as per CCTV database (8) Speed of car estimated by system (9) (10) (11) Figure (9) Input image from the camera under real time scenario (10) Correlation plot for template matching (11) Estimated speed output

6 Real Time Estimation of Vehicular Speed using Normalized Correlation 4. CONCLUSION This method can be extended to determine the speed of multiple vehicles simultaneously. Though the method demonstrates perfect results during day time, night time results may differ due to lack of illumination, leaving room for error. Hence to ascertain the speed of vehicles under various conditions like rainy days, mid-day when there is a scope for shadows or darkness, further research is required. Further a fully automated scheme if implemented will increase efficiency. REFERENCES: [1] Pratishtha Gupta, G N Purohit, ManishaRathore, Estimating Speed of Vehicle using centroid method in MATLAB, International Journal of Computer Applications, Vol.102- No.14, September 2014 [2] Siddharth Jhumat and Ravindra Kumar Purwar, Techniques to Estimate Vehicle Speed, International Journal of Advanced Research in Computer and Communication Engineering, Vol.3, Issue6, June2014. [3] Sen-Ching S. Cheung and Chandrika Kamath, Robust techniques for background subtraction in urban traffic video, Centre for Applied Scientific Computing Lawrence Livermore National Laboratory [4] Ali Taherkhani and J.Mohammadi, Object speed estimation in frequency domain of single taken image, Journal of Basic and Applied Scientific Research 3(1s) , July 2013 [5] Rahim H. A, R. B. Ahmad, A. S M Zain, U. U Sheikh An adapted point based tracking for vehicle speed estimation in linear spacing. [6] Osman Ibrahim, HazemElgendy, Ahmed M. ElShafee Speed Detection Camera system using image processing techniques on video streams. [7] AzharHussain, KashifShahzad, Chunming Tang Real time speed estimation of vehicles, International journal of computer, electrical, automation, control and information engineering, Vol. 6, No [8] S. Barnwal, R Barnwal, R Hegde and R Sing, Doppler based speed estimation of vehicles using passive sensor, IEEE international conference on Multimedia and expo workshops, 2013, pp 1-4 [9] V. K. Madasu and M. Hanmandlu, Estimation of vehicle speed by motion tracking on image sequences, Intelligent Vehicles Symposium, 2010 IEEE, pp [10] Sukdev Singh, Rishma Chawla, Harpal Singh Speed Violation Detection System: A review International Journal of Engineering Sciences & Research Technology, January [11] D. R. Magee, Tracking multiple vehicles using foreground, background, and motion models, in Proceedings of the Statistical Methods in Video Processing Workshop, pp. 7 12, (Copenhagen, Denmark), June 2002 [12] Gezim Hoxha, Ahmet Shala, Ramë Likaj, Pedestrian Crash Model for Vehicle Speed Calculation at Road Accident. International Journal of Civil Engineering and Technology, 8(9), 2017, pp [13] Gëzim Hoxha and Nijazi Ibrahimi, Implementation of Graphical - Analytical Method in Determination of Speed of Vehicle in Case of Road Accident and Comparing of Results with the Software Method, International Journal of Mechanical Engineering and Technology (IJMET), Volume 4, Issue 6, November - December (2013), pp editor@iaeme.com