  The discrimination and simplicity of features are very important for
effective and efficient pedestrian detection. However, most state-of-the-art
methods are unable to achieve good tradeoff between accuracy and efficiency.
Inspired by some simple inherent attributes of pedestrians (i.e., appearance
constancy and shape symmetry), we propose two new types of non-neighboring
features (NNF): side-inner difference features (SIDF) and symmetrical
similarity features (SSF). SIDF can characterize the difference between the
background and pedestrian and the difference between the pedestrian contour and
its inner part. SSF can capture the symmetrical similarity of pedestrian shape.
However, it's difficult for neighboring features to have such above
characterization abilities. Finally, we propose to combine both non-neighboring
and neighboring features for pedestrian detection. It's found that
non-neighboring features can further decrease the average miss rate by 4.44%.
Experimental results on INRIA and Caltech pedestrian datasets demonstrate the
effectiveness and efficiency of the proposed method. Compared to the
state-of-the-art methods without using CNN, our method achieves the best
detection performance on Caltech, outperforming the second best method (i.e.,
Checkboards) by 1.63%.
