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  1. Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis (MPVA '10)
  2. Learning human pose in crowd
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Image-based indoor positioning system: fast image matching using omnidirectional panoramic images
Real time multiple people tracking and pose estimation
Video narrative authoring with motion inpainting
Efficient person identification using active cameras in a smartroom
Learning local features for age estimation on real-life faces
Spatial-temporal understanding of urban scenes through large camera network
3d gesture recognition applying long short-term memory and contextual knowledge in a CAVE
Space speaks: towards socially and personality aware visual surveillance
Modeling and recognition of complex multi-person interactions in video
Learning human pose in crowd
Video topic modelling with behavioural segmentation

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Learning human pose in crowd

Content Provider ACM Digital Library
Author Hongeng, Somboon Gong, Shaogang Xiang, Tao
Abstract In a crowded public space, body and head pose can provide useful information for understanding human behaviours and intentions. In this paper, we propose a novel framework for locating people and inferring their body and head poses. Human detection and pose estimation are two closely related problems but have been tackled independently in previous studies. In this work, we advocate joint detection and recognition of both head and body poses. Our framework is based on learning an ensemble of pose-sensitive human body models whose outputs provide a new representation for poses. To avoid tedious and inconsistent manual annotation for learning pose-sensitive models, we formulate a semi-supervised learning method for model training which bootstraps an initial model using a small set of labelled data, and subsequently improves the model iteratively by data mining from a large unlabelled dataset. Experiments using data from a busy underground station demonstrate that the proposed method significantly outperforms a state-of-the-art person detector and is able to yield extremely accurate head and body pose estimation in crowded public spaces.
Starting Page 47
Ending Page 52
Page Count 6
File Format PDF
ISBN 9781450301671
DOI 10.1145/1878039.1878050
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-10-29
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Humen intent analysis Post estimation Person detection
Content Type Text
Resource Type Article
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