An Improved Pedestrian Detection Method Based on Adaboost and Performance Analysis

Abstract:

Pedestrian detection, which has a wide application in surveillance, advanced robotics, and especially intelligent vehicles, is an important area in computer vision. This paper applies a detection approach based on improved Adaboost algorithm. We use a dataset to train the weak classifiers (with different numbers) to cascade to be strong classifiers, in which we employ optimized strategy of sample weight adjustment to reduce the over-fit. After constructing a strong classifier, we apply different scale of sliding widow to shift and calculate the corresponding features to classify them as pedestrians or non-pedestrians. The experiments show that different numbers of weak classifiers layer and different scale of sliding windows can give different performance in detecting.

• Periodical: Advanced Materials Research (Volumes 317-319)
• Pages: 877-880
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.317-319.877
• Online since: August 2011
• Authors: Yan Xi Zhang, Xiang Dong Gao
• Keywords: Adaboost, Haar Features Sets, Pedestrian Detection

References

[1] T. Gandhi and M.M. Trived: IEEE Trans. Intelligent Transportation Systems, vol. 8, no. 3, pp.413-430, Sept. 2007.

[2] P. Viola, M. Jones, and D. Snow: Int'l J. Computer Vision, vol. 63, no. 2, pp.153-161, 2005.

[3] H. Shimizu and T. Poggio: Proc. IEEE Intelligent Vehicles Symp, pp.596-600, 2004.

[4] Yi Tang, Wei-Ming Liu and Wu JianWei: Proceedings of the 8th World Congress on Intelligent Control and Automation, pp.6321-6326, July, 2010.

DOI: 10.1109/wcica.2010.5554358

[5] L. Zhang, B. Wu, and R. Nevatia: Proc. Int'l Conf. Computer Vision, 2007.

[6] S. Munder and D. M. Gavrila: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp.1863-1868, November 2006.

DOI: 10.1109/tpami.2006.217