The analysis of neuronal network function requires a reliable measurement of behavioral traits. Since the behavior of freely moving animals is variable to a certain degree, many animals have to be analyzed, to obtain statistically significant data. This in turn requires a computer assisted automated quantification of locomotion patterns. To obtain high contrast images of almost translucent and small moving objects, a novel imaging technique based on frustrated total internal reflection called FIM was developed. In this setup, animals are only illuminated with infrared light at the very specific position of contact with the underlying crawling surface. This methodology results in very high contrast images. Subsequently, these high contrast images are processed using established contour tracking algorithms. Based on this, we developed the FIMTrack software, which serves to extract a number of features needed to quantitatively describe a large variety of locomotion characteristics. During the development of this software package, we focused our efforts on an open source architecture allowing the easy addition of further modules. The program operates platform independent and is accompanied by an intuitive GUI guiding the user through data analysis. All locomotion parameter values are given in form of csv files allowing further data analyses. In addition, a Results Viewer integrated into the tracking software provides the opportunity to interactively review and adjust the output, as might be needed during stimulus integration. The power of FIM and FIMTrack is demonstrated by studying the locomotion of Drosophila larvae.