Pedestrian Detection with Sparse Depth Estimation

Yu WANG  Jien KATO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.8   pp.1690-1699
Publication Date: 2011/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.1690
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
Keyword: 
pedestrian detection,  depth estimation,  stereo matching,  

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Summary: 
In this paper, we deal with the pedestrian detection task in outdoor scenes. Because of the complexity of such scenes, generally used gradient-feature-based detectors do not work well on them. We propose to use sparse 3D depth information as an additional cue to do the detection task, in order to achieve a fast improvement in performance. Our proposed method uses a probabilistic model to integrate image-feature-based classification with sparse depth estimation. Benefiting from the depth estimates, we map the prior distribution of human's actual height onto the image, and update the image-feature-based classification result probabilistically. We have two contributions in this paper: 1) a simplified graphical model which can efficiently integrate depth cue in detection; and 2) a sparse depth estimation method which could provide fast and reliable estimation of depth information. An experiment shows that our method provides a promising enhancement over baseline detector within minimal additional time.