Segmentation and tracking of multiple interacting mice by temperature and shape information

The study of neurological processes and pharmaceutical effects often relies on the analysis of mice behaviour. Automatic tracking tools are widely employed for this purpose, however they are mainly limited to a single mouse. We propose a real time segmentation and tracking algorithm able to manage multiple interacting mice regardless of their fur colour or light settings via an infrared camera. The approach proposed combines position, temperature and shape information thanks to the two main contributions of this paper: the “temporal watershed” and its information fusion with mice “heat signatures”. The former segments shapes thanks to an extension of a classical seed-based segmentation algorithm in a expectation maximization framework; the latter contributes in mice identities preservation through the dynamic heat distribution of each body. Preliminary results show that our algorithm achieves performance comparable to the state of art, even with a larger number of targets to be tracked.