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This paper presents an approach for data association in single camera, multi-object tracking scenarios using Feed-Forward Neural Networks (FFNN). The challenges of data association are object occlusions and changing features which are used to describe objects during the process. The presented algorithm within this article can be applied to any kind of object which has to be tracked, e.g. persons and vehicles. This approach arises within a project to detect critical behavior of persons. Besides, person tracking is one of the most challenging scenarios. People have different velocities and often change the moving direction. In addition, a variety of occlusions are caused by the movement as a group. Also in most surveillance scenarios the illumination conditions are not optimal.
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