Abstract
Aiming at the problem of the traditional AdaBoost detection, such as false detection and large amount of calculation, this paper proposes a new detection method combining Gaussian background difference and AdaBoost. First, we use Gaussian background difference and morphological processing to remove unwanted information and extract the motion area. Then we use AdaBoost algorithm to identify the target of the moving area, determine the exact location of the vehicle and record this position, so that the vehicle can still be detected when it is decelerated or stopped. Set CamShift’s initial search window to a detected vehicle position and then track the vehicle’s real time position by iteratively approximating the exact position of the target. The experimental results show that this method can achieve real-time detection and tracking of vehicles with complex background and has good robustness.
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References
Coifman, B., Beymer, D., Mc Lauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transp. Res. C Emerg. Technol. 6, 271–288 (1998)
Stojmenovic, M.: Real time machine learning based car detection in images with fast training. Mach. Vis. Appl. 17(3), 163–172 (2006)
Cao, X., Wu, C., Ran, P. et al.: Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos. In: IEEE Conference on Image Processin,. Brussels, pp. 2421−2424 (2011)
Zhao, J., Liu, C.: Aim tracking algorithms for blocking problems. J. Comput. Aided Des. Graph. 15(9), 1091–1095 (2003)
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real time tracking with shadow detection. In: European Workshop on Advanced the Video Based Surveillance Systems, pp. 149–158. Kluwer Academic Publishers, Kingston (2002)
Lienhart, R., Maydt, J.: An extended set of Haar-Like features for rapid object detection. In: Proceedings of the 2002 International Conference on Image Processing, vol. 1, pp. I-900–I-903. IEEE (2002)
Acknowledgement
This work was supported in part by State Key Laboratory of Air Traffic Management System and Technology (No: SKLATM201708).
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Cong, B., Wang, Z. (2019). Camshift Target Tracking Based on Gaussian Background Difference and Adaboost Combination. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_101
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DOI: https://doi.org/10.1007/978-3-319-93659-8_101
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