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

The work on 3D human pose estimation has been through 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 occlusion...

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Bibliographic Details
Main Authors: Qingju Liu, de Campos, Teofilo E., Wenwu Wang, Hilton, Adrian
Format: Conference Proceeding
Language:English
Subjects:
3D tracking
Clutter
Commodities
Computational efficiency
Computational modeling
Electronics
Head
identity association
Magnetic heads
Occlusion
outlier detection
Outliers (statistics)
PHD filter
Sensors
Target detection
Target tracking
Three dimensional
Three-dimensional displays
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Description
Summary:The work on 3D human pose estimation has been through 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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2016.7471928