Author:
Nathanael L. Baisa
Affiliation:
University of Lincoln, United Kingdom
Keyword(s):
Visual Tracking, Multiple Target Filtering, MHT, PHD Filter, HISP Filter, MOT Challenge.
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 multi-target visual tracker based on the recently developed Hypothesized and Independent
Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches
like multiple hypothesis tracking (MHT) and point-process-based approaches like probability hypothesis density
(PHD) filter, and has a linear complexity while maintaining track identities. We apply this filter for
tracking multiple targets in video sequences acquired under varying environmental conditions and targets density
using a tracking-by-detection approach. In addition, we alleviate the problem of two or more targets
having identical label taking into account the weight propagated with each confirmed hypothesis. Finally, we
carry out extensive experiments on Multiple Object Tracking 2016 (MOT16) benchmark dataset and find out
that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy.