Preprint Article Version 1 This version not peer reviewed

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

Version 1 : Received: 15 February 2017 / Approved: 16 February 2017 / Online: 16 February 2017 (09:39:29 CET)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Sensors 2017, 17, 501
DOI: 10.3390/s17030501

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, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Subject Areas

multi-target tracking; multi-Bernoulli filter; sequential Monte-Carlo

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