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Sensors 2017, 17(3), 501; doi:10.3390/s17030501

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

Department of Electrical & Computer Engineering, Marquette University, 1551 W. Wisconsin Ave., Milwaukee, WI 53233, USA
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Academic Editor: Vittorio M. N. Passaro
Received: 3 January 2017 / Revised: 15 February 2017 / Accepted: 27 February 2017 / Published: 3 March 2017
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: multi-target tracking; multi-Bernoulli filter; sequential Monte Carlo multi-target tracking; multi-Bernoulli filter; sequential Monte Carlo
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hoak, A.; Medeiros, H.; Povinelli, R.J. Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods. Sensors 2017, 17, 501.

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