Abstract
We propose an optimized integrated channel features which can effectively improve the detection performance at the frame rate of 30 fps on images size of 640x480. The proposed method utilizes the distribution of filter response from positive and negative features to formulate the optimized combination of multiple filters. The optimized combination coefficient is learned from linear discriminative criterion which is superior to integrated channel features with random coefficients. Experimental results based on INRIA dataset shows the superiority of our method to other state-of-arts methods.
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Shen, J., Zuo, X., Yang, W., Liu, G. (2014). Real-Time Human Detection Based on Optimized Integrated Channel Features. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_30
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DOI: https://doi.org/10.1007/978-3-662-45643-9_30
Publisher Name: Springer, Berlin, Heidelberg
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