Paper Info
Title
Pedestrian detection using GPU-accelerated multiple cue computation
Abstract
Achieving accurate pedestrian detection for practically relevant scenarios in real-time is an important problem for many applications, while representing a major scientific challenge at the same time. In this paper we present an algorithmic framework which efficiently computes pedestrian-specific shape and motion cues and combines them in a probabilistic manner to infer the location and occlusion status of pedestrians viewed by a stationary camera. The articulated pedestrian shape is represented by a set of sparse contour templates, where fast template matching against image features is carried out using integral images built along oriented scan-lines. The motion cue is obtained by employing a non-parametric background model using the YCbCr color space. Both cues are computed and evaluated on the GPU. Given the probabilistic output from the two cues the spatial configuration of hypothesized human body locations is obtained by an iterative optimization scheme taking into account the depth ordering and occlusion status of individual hypotheses. The method achieves fast computation times even in complex scenarios with a high pedestrian density. Employed computational schemes are described in detail and the validity of the approach is demonstrated on three PETS2009 datasets depicting increasing pedestrian density. Evaluation results and comparison with state of the art are presented.
Year
DOI
Venue
2011
10.1109/CVPRW.2011.5981807
CVPR Workshops
Field
DocType
Volume
Template matching,Computer vision,Pattern recognition,Feature (computer vision),Computer science,Image segmentation,Artificial intelligence,Probabilistic logic,Motion estimation,Graphics processing unit,Pedestrian detection,Computation
Conference
2011
Issue
ISSN
ISBN
1
2160-7508
978-1-4577-0529-8
Citations 
PageRank 
References 
5
0.47
19
Authors
3
Name
Order
Citations
PageRank
Csaba Beleznai136718.96
David Schreiber2515.78
Michael Rauter3172.56