Exploiting long-term observations for track creation and deletion in online multi-face tracking

Abstract

In many visual multi-object tracking applications, the question when to add or remove a target is not trivial due to, for example, erroneous outputs of object detectors or observation models that cannot describe the full variability of the objects to track. In this paper, we present a real-time, online multi-face tracking algorithm that effectively deals with missing or uncertain detections in a principled way. The tracking is formulated in a multi-object state-space Bayesian filtering framework solved with Markov Chain Monte Carlo. Within this framework, an explicit probabilistic filtering step relying on head detections, likelihood models, and long term observations as well as object track characteristics has been designed to take the decision on when to add or remove a target from the tracker. The proposed method applied on three challenging datasets of more than 9 hours shows a significant performance increase compared to a traditional approach relying on head detection and likelihood models only.