| | |
Summary: Simultaneous tracking of multiple body parts of interacting persons
Sangho Park *, J.K. Aggarwal
Computer and Vision Research Center, Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX 78712, USA
Received 18 October 2003; accepted 18 July 2005
Available online 14 November 2005
Abstract
This paper presents a framework to simultaneously segment and track multiple body parts of interacting humans in the presence of
mutual occlusion and shadow. The framework uses multiple free-form blobs and a coarse model of the human body. The color image
sequence is processed at three levels: pixel level, blob level, and object level. A Gaussian mixture model is used at the pixel level to train
and classify individual pixel based on color. Relaxation labeling in an attribute relational graph (ARG) is used at the blob level to merge
the pixels into coherent blobs and to represent inter-blob relations. A twofold tracking scheme is used that consists of blob-to-blob
matching in consecutive frames and blob-to-body-part association within a frame. The tracking scheme resembles multi-target, multi-
association tracking (MMT). A coarse model of the human body is applied at the object level as empirical domain knowledge to resolve
ambiguity due to occlusion and to recover from intermittent tracking failures. The result is ÔARGMMTÕ: Ôattribute relational graph
based multi-target, multi-association tracker.Õ The tracking results are demonstrated for various sequences including Ôpunching,Õ
Ôhand-shaking,Õ Ôpushing,Õ and ÔhuggingÕ interactions between two people. This ARGMMT system may be used as a segmentation
and tracking unit for a recognition system for human interactions.
Ó 2005 Elsevier Inc. All rights reserved.
Keywords: Tracking; Body part; Human interaction; Occlusion; ARG; MMT
1. Introduction
|
