Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry

Jiale Cao, Yanwei Pang, Xuelong Li; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1316-1324

Abstract


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 nonneighboring 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-ofthe- 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%.

Related Material


[pdf]
[bibtex]
@InProceedings{Cao_2016_CVPR,
author = {Cao, Jiale and Pang, Yanwei and Li, Xuelong},
title = {Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}