title: Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods.
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.
authors.affiliation: Department of Electrical & Computer Engineering, Marquette University, 1551 W. Wisconsin Ave., Milwaukee, WI 53233, USA. anthony.hoak@marquette.edu.
authors.forename: Anthony
authors.initials: A
authors.lastname: Hoak
authors.affiliation: Department of Electrical & Computer Engineering, Marquette University, 1551 W. Wisconsin Ave., Milwaukee, WI 53233, USA. henry.medeiros@marquette.edu.
authors.forename: Henry
authors.initials: H
authors.lastname: Medeiros
authors.affiliation: Department of Electrical & Computer Engineering, Marquette University, 1551 W. Wisconsin Ave., Milwaukee, WI 53233, USA. richard.povinelli@marquette.edu.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Povinelli
keywords: multi-Bernoulli filter; multi-target tracking; sequential Monte Carlo