Vehicle Type Recognition Algorithm Based on Improved Bag of Words Model
KANG Peipei
YU Fengqin
CHEN Ying
Abstract：Aiming at the problem that vehicle type recognition algorithms based on original bag-of-words model run slowly and inaccurately,a vehicle type recognition algorithm based on improved bag-of-words model is proposed. Firstly,Dense-Surf method is used to extract features when stratage of dense sample has being optimized to speed up the process;Secondly,a new algo-rithm named feature context-vector quantization(FC-VQ)is proposed to encode features,from which the location information of features can be expressed clearly,and the recognition rate increases synchronously. Finally,fast histogram intersection kernel is used as kernel function of SVM classifier for training and recognizing processes of encoded features.Experimental results show that the algorithm proposed in this paper has higher recognition rate and faster recognition speed compared with other vehicle type recog-nition algorithms.
Keywords：vehicle type recognitionbag-of-words modelDense-SURFFC-VQ encodingfast histogram intersection kernel
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 861-865,895 )