Authors:
Nathanael L. Baisa
and
Andrew Wallace
Affiliation:
Heriot Watt University, United Kingdom
Keyword(s):
Visual Tracking, Random Finite Set, Multiple Target Filtering, Gaussian Mixture, Tri-GM-PHD Filter, OSPA Metric.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
;
Video Surveillance and Event Detection
Abstract:
We propose a new framework that extends the standard Probability Hypothesis Density (PHD) filter for multiple
targets having three different types, taking into account not only background false positives (clutter), but
also confusion between detections of different target types, which are in general different in character from
background clutter. Our framework extends the existing Gaussian Mixture (GM) implementation of the PHD
filter to create a tri-GM-PHD filter based on Random Finite Set (RFS) theory. The methodology is applied
to real video sequences containing three types of multiple targets in the same scene, two football teams and a
referee, using separate detections. Subsequently, Munkres’s variant of the Hungarian assignment algorithm is
used to associate tracked target identities between frames. This approach is evaluated and compared to both
raw detections and independent GM-PHD filters using the Optimal Sub-pattern Assignment (OSPA) metric
and discrimination rate. This sh
ows the improved performance of our strategy on real video sequences.
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