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

Sensors

卷:17

Henry Medeiros¹  Anthony Hoak²  Richard J. Povinelli² 

[1] Computer Engineering, Marquette University, 1551 W. Wisconsin Ave., Milwaukee, WI 53233, USA;

[2] Department of Electrical &

关键词: multi-target tracking;    multi-Bernoulli filter;    sequential Monte Carlo;   

DOI  :  10.3390/s17030501

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown