Document Type
Article
Language
eng
Publication Date
3-2017
Publisher
MDPI
Source Publication
Sensors
Source ISSN
1424-8220
Abstract
We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Hoak, Anthony B.; Medeiros, Henry P.; and Povinelli, Richard J., "Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods" (2017). Electrical and Computer Engineering Faculty Research and Publications. 301.
https://epublications.marquette.edu/electric_fac/301
Comments
Published version. Sensors, Vol. 17, No. 3 (March 2017): 501 1-23. DOI. c 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY)