关键词: 可变形部件模型, 车辆检测, 加权主成分分析, 特征层组合

Abstract: Aiming at the problem that running slowly when using deformable part model to detect vehicles due to its high complexity, an improved deformable part model is proposed. On the one hand, weighted PCA is used to reduce dimension of HOG feature, the basis of deformable part model, so that the model’s parameters can be decreased. On the other hand, after the combination of HOG feature-levels, Fast Fourier Transform（FFT） is applied to convert convolution between filters and HOG feature-levels into multiplication in frequency domain, which reduces the computation complexity. The experimental results show that the improved deformable part model gains similar precision and recall-rate compared with original model, while its speed increases significantly, whose average consuming time accounts for 29.6% and 26.3% of original model respectively on UIUC dataset and BIT dataset.

Key words: deformable part model, vehicle detection, Weighted Principal Component Analysis（WPCA）, feature-levels combination