Fuzzy spatial constraints and ranked partitioned sampling approach for multiple object tracking ?
While particle filters are now widely used for object tracking in videos, the case of multiple object tracking still raises a number of issues. Among them, a first, and very important, problem concerns the exponential increase of the number of particles with the number of objects to be tracked, that can make some practical applications intractable. To achieve good tracking performances, we propose to use a Partitioned Sampling method in the estimation process with an additional feature about the ordering sequence in which the objects are processed. We call it Ranked Partitioned Sampling, where the optimal order in which objects should be processed and tracked is estimated jointly with the object state. Another essential point concerns the modeling of possible interactions between objects. As another contribution, we propose to represent these interactions within a formal framework relying on fuzzy sets theory. This allows us to easily model spatial constraints between objects, in a general and formal way. The association of these two contributions was tested on typical videos exhibiting difficult situations such as partial or total occlusions, and appearance or disappearance of objects. We show the benefit of using conjointly these two contributions, in comparison to classical approaches, through multiple object tracking and articulated object tracking experiments on real video sequences. The results show that our approach provides less tracking errors than those obtained with the classical Partitioned Sampling method, without the need for increasing the number of particles.
fuzzy spatial constraints partitioned sampling multiple object tracking particle filter