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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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2017, 17(3), 501; https://doi.org/10.3390/s17030501
Received: 3 January 2017
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Revised: 15 February 2017
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Accepted: 27 February 2017
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Published: 3 March 2017
(This article belongs to the Section Physical Sensors)
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.
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MDPI and ACS Style
Hoak, A.; Medeiros, H.; Povinelli, R.J. Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods. Sensors 2017, 17, 501. https://doi.org/10.3390/s17030501
AMA Style
Hoak A, Medeiros H, Povinelli RJ. Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods. Sensors. 2017; 17(3):501. https://doi.org/10.3390/s17030501
Chicago/Turabian StyleHoak, Anthony, Henry Medeiros, and Richard J. Povinelli. 2017. "Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods" Sensors 17, no. 3: 501. https://doi.org/10.3390/s17030501
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