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Identity association using PHD filters in multiple head tracking with depth sensors

Liu, Q, deCampos, T, Wang, W and Hilton, A (2016) Identity association using PHD filters in multiple head tracking with depth sensors In: ICASSP, 2016-03-20 - 2016-03-25.

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Abstract

The work on 3D human pose estimation has seen a significant amount of progress in recent years, particularly due to the widespread availability of commodity depth sensors. However, most pose estimation methods follow a tracking-as-detection approach which does not explicitly handle occlusions, thus introducing outliers and identity association issues when multiple targets are involved. To address these issues, we propose a new method based on Probability Hypothesis Density (PHD) filter. In this method, the PHD filter with a novel clutter intensity model is used to remove outliers in the 3D head detection results, followed by an identity association scheme with occlusion detection for the targets. Experimental results show that our proposed method greatly mitigates the outliers, and correctly associates identities to individual detections with low computational cost.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Liu, QUNSPECIFIEDUNSPECIFIED
deCampos, TUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Hilton, AUNSPECIFIEDUNSPECIFIED
Date : 20 March 2016
Identification Number : 10.1109/ICASSP.2016.7471928
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 06 Sep 2016 16:44
Last Modified : 06 Sep 2016 16:44
URI: http://epubs.surrey.ac.uk/id/eprint/811759

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