Paper Info
Title
Low resolution pedestrian detection using light robust features and hierarchical system
Abstract
The pedestrian detection is a popular research field in recent years, yet the low-resolution issue is rarely discussed for yielding detection accuracy for drivers. In this study, a hierarchical pedestrian detection system is proposed to cope with this issue. In which, two independent features, orientation and magnitude, are adopted as descriptors for pedestrians. Moreover, the proposed probability-based pedestrian mask pre-filtering (PPMPF) is utilized to initially filter out non-pedestrian regions meanwhile retaining most of the real pedestrians. In experimental results, the use of the two proposed features can provide superior performance than the former well-known histogram of oriented gradient (HOG; high accuracy) and the edgelet (high processing efficiency) simultaneously without carrying their lacks. Moreover, the PPMPF can also boost the processing efficiency by a factor of around 2.82 in contrast to the system without this pre-filtering strategy. Thus, the proposed method can be a very competitive candidate for intelligent surveillance applications.
Year
DOI
Venue
2014
10.1016/j.patcog.2013.11.008
Pattern Recognition
Keywords
Field
DocType
low resolution pedestrian detection,high processing efficiency,hierarchical system,real pedestrian,detection accuracy,hierarchical pedestrian detection system,low-resolution issue,pedestrian detection,proposed probability-based pedestrian mask,proposed feature,light robust feature,high accuracy,adaboost,pattern recognition,computer vision
Hierarchical control system,Histogram,Computer vision,Pedestrian,AdaBoost,Pattern recognition,Artificial intelligence,Pedestrian detection,Mathematics
Journal
Volume
Issue
ISSN
47
4
0031-3203
Citations 
PageRank 
References 
4
0.41
17
Authors
3
Name
Order
Citations
PageRank
Yun-Fu Liu127719.65
Jing-Ming Guo283077.60
Che-Hao Chang3202.50