Human body part detection using likelihood score computations

Human body part detection using likelihood score computations
Title:
Human body part detection using likelihood score computations
Other Titles:
2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)
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Publication Date:
09 December 2014
Citation:
M. Ramanathan, W. Y. Yau and E. K. Teoh, "Human body part detection using likelihood score computations," 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM), Orlando, FL, 2014, pp. 160-166. doi: 10.1109/CIBIM.2014.7015458
Abstract:
Detection and labelling of human body parts in videos or images can provide vital clues in analysis of human behaviour and action. Detecting body parts separately is considerably difficult due to the huge amount of intra-class variations exhibited. In most methods, researchers tend to impose some connectivity or shape constraints on the classifier output to obtain the final detected body parts. In this paper, we propose a novel idea to compute likelihood scores for each of the initial classified body parts based on Bayes theorem using Extreme learning machine's (ELM) output value (different from the predicted class label). Also, we do not impose any other constraints on the initially detected body parts. We use Histogram of oriented gradients (HOG) features and ELM for initial classification. We also employ a voting scheme that uses inter-frame detected segments to filter out errors and detect body parts in the current frame. Experiments have been conducted to show our method can identify body parts in different body postures quite appreciably.
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PublisherCopyrights
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Description:
(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISBN:
978-1-4799-4533-7
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