Abstract
In this paper, we introduce a tracking algorithm based on labeled Random Finite Sets (RFS) and Rauch–Tung–Striebel (RTS) smoother via a Generalized Labeled Multi-Bernoulli (GLMB) multi-scan estimator to track multiple objects in a wide range of tracking scenarios. In the forward filtering stage, we use the GLMB filter to generate a set of labels and the association history between labels and the measurements. In the trajectory-estimating stage, we apply a track management strategy to eliminate tracks with short lifespan compared to a threshold value. Subsequently, we apply the information of trajectories captured from the forward GLMB filtering stage to carry out standard forward filtering and RTS backward smoothing on each estimated trajectory. For the experiment, we implement the tracker with standard GLMB filter, the hybrid track-before-detect (TBD) GLMB filter, and the GLMB filter with objects spawning. The results show improvements in tracking performance for all implemented trackers given negligible extra computational effort compared to standard GLMB filters.
Highlights
While single-object tracking algorithms have been studied extensively for more than half a century, multi-object tracking is currently a trending topic in signal processing society due to its extensive applications
Random Finite Sets (RFS) forms the mathematical basis of many modern multi-object filters such as Probability Hypothesis Density (PHD) filter [3,4,5,6,7], cardinalized PHD (CPHD) filter [8,9,10], multi-Bernoulli filter [11,12], the Generalized
In the context of RFS-based filtering techniques, such spawning models have been proposed for CPHD filter in References [28,29] and for Generalized Labeled Multi-Bernoulli (GLMB) filter in Reference [30]
Summary
While single-object tracking algorithms have been studied extensively for more than half a century, multi-object tracking is currently a trending topic in signal processing society due to its extensive applications. The challenges of the multi-object tracking problem arise in the context of miss-detection, false alarms, object thinning, and appearing processes To tackle these problems, several frameworks have been put forward in the literature such as the Joint Probabilistic Data Association (JPDA) [1], multiple hypotheses tracking [2], and recently, Random Finite Sets (RFS) [2]. In many applications, tracking algorithms rely on the standard point measurements to update the object states; in contrast, TBD [22,23,24,25] is an alternative approach that bypasses the detection module to directly exploit the observed spatial data This technique is introduced under the RFS framework in Reference [26] with the development of the so-called separable likelihood model and, recently, in a hybrid (combination of standard observation and separable observation models) approach in Reference [27]. The “+” sign is used to indicate the time step when applicable
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