Title
Exploring Human Vision Driven Features for Pedestrian Detection
Abstract
Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture. Our main contributions are first to design a local, statistical multi-channel descriptor in order to incorporate both color and gradient information. Second, we introduce a multi-direction and multi-scale contrast scheme based on grid-cells in order to integrate expressive local variations. Contributing to the issue of selecting most discriminative features for assessing and classification, we perform extensive comparisons w.r.t. statistical descriptors, contrast measurements, and scale structures. This way, we obtain reasonable results under various configurations. Empirical findings from applying our optimized detector on the INRIA and Caltech pedestrian datasets show that our features yield state-of-the-art performance in pedestrian detection.
Year
DOI
Venue
2015
10.1109/TCSVT.2015.2397199
Circuits and Systems for Video Technology, IEEE Transactions  
Keywords
Field
DocType
center-surround contrast,channels,human vision,multi-direction,multi-scale,pedestrian detection.,vectors,gaussian distribution,histograms,detectors,feature extraction,visualization
Computer vision,Histogram,Pedestrian,Grid cell,Pattern recognition,Computer science,Visualization,Feature extraction,Artificial intelligence,Detector,Pedestrian detection,Discriminative model
Journal
Volume
Issue
ISSN
PP
99
1051-8215
Citations 
PageRank 
References 
9
0.51
36
Authors
4
Name
Order
Citations
PageRank
Shanshan Zhang123121.21
Christian Bauckhage21979195.86
Dominik A. Klein390.51
Armin B. Cremers423639.11