Author: Wongun Choi, Silvio Savarese.
Abstract:
Tracking multiple objects is important in many application
domains. We propose a novel algorithm for multi-object tracking that
is capable of working under very challenging conditions such as min-
imal hardware equipment, uncalibrated monocular camera, occlusions
and severe background clutter. To address this problem we propose a
new method that jointly estimates object tracks, estimates correspond-
ing 2D/3D temporal trajectories in the camera reference system as well
as estimates the model parameters (pose, focal length, etc) within a
coherent probabilistic formulation. Since our goal is to estimate stable
and robust tracks that can be univocally associated to the object IDs,
we propose to include in our formulation an interaction (attraction and
repulsion) model that is able to model multiple 2D/3D trajectories in
space-time and handle situations where objects occlude each other. We
use a MCMC particle ltering algorithm for parameter inference and
propose a solution that enables accurate and e cient tracking and cam-
era model estimation. Qualitative and quantitative experimental results
obtained using our own dataset and the publicly available ETH dataset
shows very promising tracking and camera estimation results.
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