In this paper, we present a stochastic framework for articulated 3D human motion tracking. Tracking full body
human motion is a challenging task, because the tracking performance normally suffers from several issues such
as self-occlusion, foreground segmentation noise and high computational cost. In our work, we use explicit
3D reconstructions of the human body based on a visual hull algorithm as our system input, which effectively
eliminates self-occlusion. To improve tracking efficiency as well as robustness, we use a Kalman particle filter
framework based on an interacting multiple model (IMM). The posterior density is approximated by a set of
weighted particles, which include both sample means and covariances. Therefore, tracking is equivalent to
searching the maximum a posteriori (MAP) of the probability distribution. During Kalman filtering, several
dynamical models of human motion (e.g., zero order, first order) are assumed which interact with each other
for more robust tracking results. Our measurement step is performed by a local optimization method using
simulated physical force/moment for 3D registration. The likelihood function is designed to be the fitting score
between the reconstructed human body and our 3D human model, which is composed of a set of cylinders.
This proposed tracking framework is tested on a real motion sequence. Our experimental results show that
the proposed method improves the sampling efficiency compared with most particle filter based methods and
achieves high tracking accuracy.
KEYWORDS: Cameras, 3D modeling, Calibration, Shape analysis, Surveillance systems, 3D image processing, Databases, 3D metrology, Video surveillance, Surveillance
We propose a 3D surveillance system using multiple cameras surrounding the scene. Our application is concerned
with identifying humans in the scene and then identifying their postures. Such information can help with
automatic threat assessment of a scene. The cameras are fully calibrated and assumed to remain fixed in their
positions. Object detection and interpretation are performed completely in 3D space. Using depth information,
persons can easily be separated from the background and their posture identified by matching with 3D model
templates.
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