Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Multilabel Random Walker Image Segmentation Using Prior Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A binary variable model for affinity propagation
Neural Computation
Computer vision-based human body segmentation and posture estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Probabilistic posture classification for Human-behavior analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, we present an automatic and accurate pedestrian segmentation algorithm by incorporating pedestrian shape prior into the random walks segmentation algorithm. The random walks [1] algorithm requires user-specified labels to produce segmentation with each pixel assigned to a label, and it can provide satisfactory segmentation result with proper input labeled seeds. To take advantage of this interactive segmentation algorithm, we improve the random walks segmentation algorithm by incorporating prior shape information into the same optimization formulation. By using the human shape prior, we develop a fully automatic pedestrian image segmentation algorithm. Our experimental results demonstrate that the proposed algorithm significantly outperforms the previous segmentation methods in terms of pedestrian segmentation accuracy on a number of real images.