Authors:
Nicolai Wojke
and
Dietrich Paulus
Affiliation:
University of Koblenz-Landau, Germany
Keyword(s):
Visual Tracking, Multi-Object State Estimation.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
;
Tracking and Visual Navigation
Abstract:
The Probability Hypothesis Density Filter (PHD) filter is an efficient recursive multi-object state estimator that
systematically deals with data association uncertainty. In this paper, we apply the PHD filter in a tracking-bydetection
framework. In order to mimic state-dependent false alarms, we introduce an adapted PHD recursion
that defines clutter generators in state space. Further, we integrate detector confidence scores into the measurement
likelihood. This extension is quite effective yet simple, which means that it requires few changes to the
original PHD recursion, that it has the same computational complexity, and that there exist few parameters that
must be adapted to the individual tracking scenario. Our evaluation on a popular pedestrian tracking dataset
demonstrates results that are competitive with the state-of-the-art.