Abstract:
Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent
vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor
bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance
cost between local tracks from different sensors, without exploiting the geometric relationship
between one track and its neighboring ones from each sensor. However, the relative geometry among
neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the
T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation
(LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks
of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding
local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical
descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track
association problem is formulated as a maximum likelihood estimation problem with a local track
geometry constraint, and an expectation–maximization (EM) algorithm is developed to find the
solution. Simulation results demonstrate that the proposed methods offer better performance than
the state-of-the-art methods.
