Fast Pedestrian Detection Via Random Projection Features With Shape Prior

Accurate pedestrian detection with high speed is always of great interests especially for practical application. Detectors usually follow the feature selection paradigm, and need to first construct rich and diverse features. In particular, current state-of-the-arts generate more channels of feature by convolving the basic feature channels with filter banks, which significantly improves accuracy. In this paper, we propose to apply random projection over the basic feature channels, implicitly selecting feature from a much larger feature space. Our method is more efficient than the ones employing filter banks by avoiding the convolution operation. We further impose shape prior to guide the random projection, making the generated feature be more robust to occlusion, pose variation and scale change. Experimental results on Caltech pedestrian dataset demonstrate the accuracy and efficiency of our method. Compared with the-state- of-arts, our method can achieve 5-10x speedup with comparable accuracy.