IEEE Trans Image Process - Track creation and deletion framework for long-term online multiface tracking.

Tópicos

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Resumo

To improve visual tracking, a large number of papers study more powerful features, or better cue fusion mechanisms, such as adaptation or contextual models. A complementary approach consists of improving the track management, that is, deciding when to add a target or stop its tracking, for example, in case of failure. This is an essential component for effective multiobject tracking applications, and is often not trivial. Deciding whether or not to stop a track is a compromise between avoiding erroneous early stopping while tracking is fine, and erroneous continuation of tracking when there is an actual failure. This decision process, very rarely addressed in the literature, is difficult due to object detector deficiencies or observation models that are insufficient to describe the full variability of tracked objects and deliver reliable likelihood (tracking) information. This paper addresses the track management issue and presents a real-time online multiface tracking algorithm that effectively deals with the above difficulties. The tracking itself is formulated in a multiobject state-space Bayesian filtering framework solved with Markov Chain Monte Carlo. Within this framework, an explicit probabilistic filtering step decides when to add or remove a target from the tracker, where decisions rely on multiple cues such as face detections, likelihood measures, long-term observations, and track state characteristics. The method has been applied to three challenging data sets of more than 9 h in total, and demonstrate a significant performance increase compared to more traditional approaches (Markov Chain Monte Carlo, reversible-jump Markov Chain Monte Carlo) only relying on head detection and likelihood for track management.

Resumo Limpo

improv visual track larg number paper studi power featur better cue fusion mechan adapt contextu model complementari approach consist improv track manag decid add target stop track exampl case failur essenti compon effect multiobject track applic often trivial decid whether stop track compromis avoid erron earli stop track fine erron continu track actual failur decis process rare address literatur difficult due object detector defici observ model insuffici describ full variabl track object deliv reliabl likelihood track inform paper address track manag issu present realtim onlin multifac track algorithm effect deal difficulti track formul multiobject statespac bayesian filter framework solv markov chain mont carlo within framework explicit probabilist filter step decid add remov target tracker decis reli multipl cue face detect likelihood measur longterm observ track state characterist method appli three challeng data set h total demonstr signific perform increas compar tradit approach markov chain mont carlo reversiblejump markov chain mont carlo reli head detect likelihood track manag

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