Tracking and data association
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of the EM algorithm for the multitarget/multisensortracking problem
IEEE Transactions on Signal Processing
Expectation maximization algorithms for MAP estimation of jumpMarkov linear systems
IEEE Transactions on Signal Processing
Probabilistic multi-class scene flow segmentation for traffic scenes
Proceedings of the 32nd DAGM conference on Pattern recognition
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In this paper a novel approach for the interdependent task of multiple object tracking and scene segmentation is presented. The method partitions a stereo image sequence of a dynamic 3-dimensional (3D) scene into its most prominent moving groups with similar 3D motion. The unknown set of motion parameters is recursively estimated using an iterated extended Kalman filter (IEKF) which will be derived from the expectation-maximization (EM) algorithm. The EM formulation is used to incorporate a probabilistic data association measure into the tracking process. In a subsequent segregation step, each image point is assigned to the object hypothesis with maximum a posteriori (MAP) probability. Within the association process, which is implemented as labeling problem, a Markov Random Field (MRF) is used to express our expectations on spatial continuity of objects.