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

Qingju Liu; Teófilo Emídio de Campos; Wenwu Wang; Adrian Hilton

ICASSP 2016accepted0 citations

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

Qingju Liu, Teófilo Emídio de Campos, Wenwu Wang, Adrian Hilton

Abstract

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.

BibTeX

@inproceedings{icassp2016_identityassociat,
  title = {Identity association using PHD filters in multiple head tracking with depth sensors},
  author = {Qingju Liu and Teófilo Emídio de Campos and Wenwu Wang and Adrian Hilton},
  booktitle = {ICASSP 2016},
  year = {2016}
}