Abstract
To briskly detect people and vehicle on the road in a video sequence is a challenging problem. Most researches focus on detecting or tracking of specific targets only. Detection and tracking of vehicles have a great demand in video surveillance and traffic management applications. Each and every vehicle in the scene must be observed, while dealing with the traffic scenarios, which solves the problem occurred due to the traffic density in an area, is high due to occlusion caused by the large number of vehicles being observed. This paper portrays the computer vision-based vehicle detection and tracking for real-time scenarios. The proposed algorithm uses the blob analysis and tracking based on a correlation filter. Here, HOG is used for feature extraction, improvised correlation filter is used for tracking and AdaBoost classifier used for classifying the vehicle. The proposed system successfully tracks and counts the vehicles during and after occlusion with other vehicles which is shown in the result.
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References
Anandhalli M, Baligar VP (2018) A novel approach in real-time vehicle detection and tracking using Raspberry Pi. Alexandria Eng J 57:1597–1607
Senthil Murugan A, Suganya Devi K, Sivaranjani A, Srinivasan P (2018) A study on various methods used for video summarization and moving object detection for video surveillance applications. In: Multimedia tools applications. Springer, Berlin
Ershadi NY, Menéndez JM (2017) Vehicle tracking and counting system in dusty weather with vibrating camera conditions. Hindawi, J Sens 1–9
Bedruz RA, Sybingco E, Bandala A, Quiros AR, Uy AC, Dadios E (2017) Real-time vehicle detection and tracking using a mean-shift based blob analysis and tracking approach. In: IEEE 9th international conference on humanoid, nanotechnology, information technology, communication and control, environment and management (HNICEM)
Coifmana B, Beymerb D, McLauchlanb P, Malik J (1998) A real-time computer vision system for vehicle tracking and traffic surveillance. Transp Res Part C 6:271–288
Ray D, Saxena A, Samal A (2018) Vehicle detection by image processing using MATLAB: a colour based approach. Int J Ind Electron Electr Eng 6:65–67
Chen L, Ye F, Ruan Y, Fan H, Chen Q (2018) An algorithm for highway vehicle detection based on convolutional neural network. EURASIP J Image Video Process 109:1–7
Dinh H, Tang H (2017) Development of a tracking-based system for automated traffic data collection for roundabouts. J Mod Transp Japan 25(1):12–23
Zhang X, Gao H, Xue C, Zhao J, Liu Y (2018) Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters. Int J Adv Rob Syst 1–9
Velazquez-Pupo R, Sierra-Romero A, Torres-Roman D, Shkvarko YV, Santiago-Paz J, Romero-Delgado M (2018) Vehicle detection with occlusion handling, tracking, and OC-SVM classification: a high performance vision-based system. Sensors 18:1–23
Sivaranjani A, Senthilrani S, Ashokumar B, Senthil Murugan A (2018) An improvised algorithm for computer vision based cashew grading system using deep CNN. In: IEEE international conference on systems, computation, automation and networking (ICSCAN), pp 555–559
Sivaranjani A, Senthilrani S, Ashokumar B, Senthil Murugan A (2019) CashNet-15: an optimized cashew nut grading using deep CNN and data augmentation. In: IEEE international conference on systems computation automation and networking, pp 768–771
Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: IEEE computer society conference on computer vision and pattern recognition, pp 1–10
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Senthil Murugan, A., Manoj Kumar, S., Senthilrani, S., Sivaranjani, A. (2021). Computer Vision-Based Vehicle Detection and Tracking. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds) Advances in Automation, Signal Processing, Instrumentation, and Control. i-CASIC 2020. Lecture Notes in Electrical Engineering, vol 700. Springer, Singapore. https://doi.org/10.1007/978-981-15-8221-9_271
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DOI: https://doi.org/10.1007/978-981-15-8221-9_271
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