Abstract
This paper presents a real time vehicle detection framework using learning method. This framework combines offline multiclass support vector machine and online boosting method for vehicle detection. Compare to tradition approaches, the proposed method can robust deal with multiclass vehicles and unfamiliar environment. Experiments with the city vehicle database are used to evaluate this method. The experimental results demonstrate the consistent robustness and efficiency of the proposed method.
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References
Sun ZH, Bebi G, Miller R (2006) On-road vehicle detection: a review. IEEE Trans Pattern Anal Mach Intell 28(5):694–711
Trivedi MM, Gandhi T, McCall J (2007) Looking-in and looking-out of a vehicle: computer-vision-based enhanced vehicle safety. IEEE Trans Intell Transp Syst 8(1):108–120
Morris BT, Trivedi MM (2008) Learning, modeling, and classification of vehicle track patterns from live video. IEEE Trans Intell Transp Syst 9(3):425–437
Chang WC, Cho CW (2010) On-line boosting for vehicle detection. IEEE Trans Syst Man Cybern Part B (Cybern) 40(3):892–902
Sivaraman S, Trivedi MM (2010) A general active-learning framework for on-road vehicle recognition and tracking. IEEE Trans Intell Transp Syst 11(2):267–276
Saffari A, Godec M, Pock T et al (2010) Online multi-class LPBoost. In: IEEE conference on computer vision and pattern recognition
Grabner H, Bischof H (2006) On-line boosting and vision. In: IEEE conference on computer vision and pattern recognition
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(27):1–27
Knerr S, Personnaz L, Dreyfus G (1990) Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman J (ed) Neu-rocomputing: algorithms, architectures and applications. Springer, New York
Acknowledgments
This work was supported by the Natural Science Foundation of Yunnan Province, China (No. 2011FZ187).
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© 2013 Springer-Verlag Berlin Heidelberg
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Qian, Z., Yang, J., Duan, L. (2013). Multiclass Vehicle Detection Based on Learning Method. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38466-0_8
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DOI: https://doi.org/10.1007/978-3-642-38466-0_8
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