Detection of pedestrian and different types of vehicles using image processing

Document Type: Research Paper


Department of Master of Computer Application, Jain Deemed to be University, Bengalore, Karnataka, India.


Nowadays, the control of the traffic in the urban roads and in the highway has been a big challenge as the number of increase in the auto mobiles. So to overcome this problem we use the detection and tracking the vehicles using the traffic surveillance system. We can manage and control the traffic more easily. It is very complicated and a challenging task to identify the vehicle or a moving object in a complex environment with various background. The ratio detected of such algorithms depends on the quality of the foreground mask generated. Therefore this project is to present the detection and tracking the vehicles and the pedestrians in an efficient method which focus on trajectory motion of the vehicles and the pedestrians. In this proposed method, the pixels in the background are preserved which can be cars, bikes, buses, pedestrian, etc., the rest is discarded as the noise. Hence, our proposed method detects the vehicles and the pedestrians as mentioned and discards the rest noise as well in the same time. Here the quality of the generated foreground mask is more to increase the detection ratio. The performance is compared with other standard methods qualitatively and quantitatively.


Main Subjects

[1]      Murphy, G. C. (2010, November). Human-centric software engineering. Proceedings of the FSE/SDP workshop on future of software engineering research (pp. 251-254).

[2]      Mohapatra, H. (2018). C Programming: practice, Vols. 9781726820875, Kindle.

[3]      Mohapatra, H., & Rath, A. (2020). Advancing generation Z employability through new forms of learning: quality assurance and recognition of alternative credentials.

[4]      Mohapatra, H., & Rath, A. (2020). Fundamentals of software engineering: designed to provide an insight into the software engineering concepts.

[5]      Ande, V. K., & Mohapatra, H. (2015). SSO mechanism in distributed environment. International journal of innovations & advancement in computer science, 4 (6), 133-136.

[6]      Mohapatra, H. (2019). Ground level survey on sambalpur in the perspective of smart water (No. 1918). EasyChair.

[7]      Mohapatra, H., Panda, S., Rath, A., Edalatpanah, S., & Kumar, R. (2020). A tutorial on powershell pipeline and its loopholes. International journal of emerging trends in engineering research8(4), 975-982.

[8]      Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol. IET wireless sensor systems9(6), 358-365.

[9]      Mohapatra, H., Debnath, S., & Rath, A. K. (2019). Energy management in wireless sensor network through EB-LEACH. International journal of research and analytical reviews (IJRAR), 56-61.

[10]   Mohapatra, H., Rath, S., Panda, S., & Kumar, R. (2020). Handling of man-in-the-middle attack in WSN through intrusion detection system. International journal8(5).

[11]   Mohapatra, H., & Rath, A. K. (2019). Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT. IET wireless sensor systems9(6), 447-457.

[12]   Panda, M., Pradhan, P., Mohapatra, H., & Barpanda, N. K. (2019). Fault tolerant routing in heterogeneous environment. International journal of scientific & technology research8, 1009-1013.

[13]   Swain, D., Ramkrishna, G., Mahapatra, H., Patr, P., & Dhandrao, P. M. (2013). A novel sorting technique to sort elements in ascending order. International journal of engineering and advanced technology3(1), 212-126.

[14]   Mohapatra, H. I. T. E. S. H. (2009). HCR using neural network (PhDdissertation, Biju Patnaik University of Technology).

[15]   Dabhade, S. B., Kazi, M. M., Rode, Y. S., Manza, R. R., & Kale, K. V. (2013, April). Face recognition using principle component analysis and linear discriminant analysis: comparative study. 2nd national conference on advancements in the era of multi-disciplinary systems AEMDS-2013. Elsevier.

[16]   Kumar, R., Edalatpanah, S. A., Jha, S., Gayen, S., & Singh, R. (2019). Shortest path problems using fuzzy weighted arc length. International journal of innovative technology and exploring engineering8(6), 724-731.

[17]   Kumar, R., Jha, S., & Singh, R. (2020). A different approach for solving the shortest path problem under mixed fuzzy environment. International journal of fuzzy system applications (IJFSA)9(2), 132-161.

[18]   Kumar, R., Jha, S., & Singh, R. (2017). Shortest path problem in network with type-2 triangular fuzzy arc length. Journal of applied research on industrial engineering4(1), 1-7.

[19]   Broumi, S., Dey, A., Talea, M., Bakali, A., Smarandache, F., Nagarajan, D., ... & Kumar, R. (2019). Shortest path problem using Bellman algorithm under neutrosophic environment. Complex & intelligent systems5(4), 409-416.

[20]   Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A multi objective programming approach to solve integer valued neutrosophic shortest path problems. Neutrosophic sets and systems24, 134-149.

[21]   Kumar, R., Dey, A., Broumi, S., & Smarandache, F. (2020). A study of neutrosophic shortest path problem. In Neutrosophic graph theory and algorithms (pp. 148-179). IGI Global.

[22]   Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. Infinite study.

[23]   Kumar, R., Edaltpanah, S. A., Jha, S., & Broumi, S. (2018). Neutrosophic shortest path problem. Neutrosophic sets and systems23(1), 2.

[24]   Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A Pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems5(2), 255-263.

[25]   Pratihar, J., Kumar, R., Dey, A., & Broumi, S. (2020). Transportation problem in neutrosophic environment. In Neutrosophic graph theory and algorithms (pp. 180-212). IGI Global.

[26]   Pratihar, J., Kumar, Edalatpanah, S. A., & Dey, A. (2020). Modified Vogel’s Approximation Method algorithm for transportation problem under uncertain environment. Complex & intelligent systems.

[27]   Sakhnini, J., Karimipour, H., Dehghantanha, A., Parizi, R. M., & Srivastava, G. (2019). Security aspects of internet of things aided smart grids: a bibliometric survey. Internet of things, 100111.

[28]   Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Interval-valued neutrosophic subgroup based on interval-valued triple t-norm. In Neutrosophic sets in decision analysis and operations research (pp. 215-243). IGI Global.

[29]   Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to pathogenic subgroup. In Smarandache, F., & Broumi, S. (Eds.), Neutrosophic graph theory and algorithm. IGI-Global.

[30]   Gayen, S., Jha, S., Singh, M., & Kumar, R. (2019). On a generalized notion of anti-fuzzy subgroup and some characterizations. International journal of engineering and advanced technology8, 385-390.

[31]   Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic hypersoft subgroup. Neutrosophic sets and systems33(1), 14.

[32]   Gayen, S., Jha, S., & Singh, M. (2019). On direct product of a fuzzy subgroup with an anti-fuzzy subgroup. International journal of recent technology and engineering8, 1105-1111.

[33]   Behura, A., & Mohapatra, H. (2019). IoT based smart city with vehicular safety Monitoring. EasyChair. Retrieved from file:///C:/Users/jpour/Downloads/EasyChair-Preprint-1535.pdf

[34]   Harris, J. L. (1966). Image evaluation and restoration. JOSA56(5), 569-574.

[35]   Billingsley, F. C. (1970). Applications of digital image processing. Applied optics9(2), 289-299.

[36]   Roesser, R. (1975). A discrete state-space model for linear image processing. IEEE transactions on automatic control20(1), 1-10.

[37]   Bernstein, R. (1976). Digital image processing of earth observation sensor data. IBM journal of research and development20(1), 40-57.

[38]   Besag, J. (1989). Digital image processing: towards bayesian image analysis. Journal of applied statistics16(3), 395-407.

[39]   Sun, T., & Neuvo, Y. (1994). Detail-preserving median based filters in image processing. Pattern recognition letters15(4), 341-347.

[40]   Smith, S. M., & Brady, J. M. (1997). SUSAN—a new approach to low level image processing. International journal of computer vision23(1), 45-78.

[41]   G.  Tang,   L.  Peng,  P.   R.  Baldwin,  D.  S.  Mann,  W.  Jiang,  I.  Rees,  and  S.  J.  Ludtke,  “Eman2:         An extensible image processing suite for electron microscopy,”  Journal of  Structural  Biology,  vol. 157, no. 1, pp. 38 – 46, 2007, software tools for macromolecular microscopy.

[42]   Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., ... & Marconcini, M. (2009). Recent advances in techniques for hyperspectral image processing. Remote sensing of environment113, S110-S122.

[43]   Elad, M., Figueiredo, M. A., & Ma, Y. (2010). On the role of sparse and redundant representations in image processing. Proceedings of the IEEE98(6), 972-982.

[44]   Green, L. W. (1966). The Pythagorean group and ergodic flows. Bulletin of the American mathematical society72(1), 44-49.

[45]   Ter Haar, D. (1989). Large-scale structures in turbulent fluids. Physica scripta39(6), 731.

[46]   Hanai, Y., Hori, Y., Nishimura, J., & Kuroda, T. (2009, February). A versatile recognition processor employing Haar-like feature and cascaded classifier. 2009 IEEE international solid-state circuits conference-digest of technical papers (pp. 148-149). IEEE.

[47]   Macomber, J. D. (1968). How does a crossed-coil NMR spectrometer work? Spectroscopy letters1(3), 131-137.

[48]   McCormack, D. K., Brown, B. M., & Pedersen, J. F. (1993, August). Neural network signature verification using Haar wavelet and Fourier transforms. In Machine vision applications, architectures, and systems integration II (Vol. 2064, pp. 14-25). International Society for Optics and Photonics.

[49]   Nesic, Z., Davies, M., & Dumont, G. (1996). Paper machine data compression using wavelets. Proceeding of the 1996 IEEE international conference on control applications IEEE international conference on control applications held together with IEEE international symposium on intelligent contro (pp. 161-166). IEEE.

[50]   Paliy, I. (2008, February). Face detection using Haar-like features cascade and convolutional neural network. 2008 international conference on" modern problems of radio engineering, telecommunications and computer science"(TCSET) (pp. 375-377). IEEE.

[51]   Reis, J. J., Lynch, R. T., & Butman, J. (1976, December). Adaptive Haar transform video bandwidth reduction system for RPV's. In Advances in image transmission techniques (Vol. 87, pp. 24-35). International Society for Optics and Photonics.

[52]   Pyimagesearch. (2020). Gray image of cars. Retrieved from

[53]   Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). IEEE.