1. Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
- Author
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Anthony Hoak, Henry Medeiros, and Richard J. Povinelli
- Subjects
multi-target tracking ,multi-Bernoulli filter ,sequential Monte Carlo ,Chemical technology ,TP1-1185 - 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.
- Published
- 2017
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