Multiple object tracking in unknown backgrounds with labeled random finite sets
MetadataShow full item record
This paper proposes an online multiple object tracker that can operate under unknown detection profile and clutter rate. In a majority of multiple object tracking applications, model parameters for background processes such as clutter and detection are unknown and vary with time; hence, the ability of the algorithm to adaptively learn these parameters is essential in practice. In this paper, we detail how the generalized labeled multibernoulli filter, a tractable and provably Bayes optimal multiobject tracker, can be tailored to learn clutter and detection parameters on-the-fly while tracking. Provided that these background model parameters do not fluctuate rapidly compared to the data rate, the proposed algorithm can adapt to the unknown background yielding better tracking performance.
Showing items related by title, author, creator and subject.
Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequencesRezatofighi, S.; Gould, S.; Vo, Ba-Ngu; Vo, Ba Tuong; Mele, K.; Hartley, R. (2015)© 2015 IEEE. Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking ...
Abd El-Sallam, Amar (2005)New approaches and algorithms are developed for the identification and estimation of low order models that represent multipath channel effects in Code Division Multiple Access (CDMA) communication systems. Based on these ...
Albrecht, Thomas (2012)Mobile surveillance systems play an important role to minimise security and safety threats in high-risk or hazardous environments. Providing a mobile marine surveillance platform with situational awareness of its environment ...