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  1. Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis (MPVA '10)
  2. Real time multiple people tracking and pose estimation
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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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Real time multiple people tracking and pose estimation

Content Provider ACM Digital Library
Author Huo, Feifei Hendriks, Emile A.
Abstract In this paper we present a combined probability estimation approach to detect and track multiple people for pose estimation at the same time. It can deal with partial and total occlusion between persons by adding torso appearance to the tracker. Moreover, the upper body of each individual is further segmented into head, torso, upper arm and lower arm in a hierarchical way. The simplicity of the feature and the simplified model allow close real time performance of the tracker. The experimental results show that the proposed method can deal with most of the inner-occlusion between persons, as well as certain self-occlusion. It's also much faster than the existing methods with comparable accuracy.
Starting Page 5
Ending Page 10
Page Count 6
File Format PDF
ISBN 9781450301671
DOI 10.1145/1878039.1878042
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-10-29
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Multiple people tracking Human pose estimation Particle filtering Probability estimation
Content Type Text
Resource Type Article
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