Model-based tracking of laboratory animals

• Main Author: Kalafatic, Z.
• Format: Conference Proceeding
• Language: English
• Subjects: Active contours; Active shape model; Animals; Gravity; Hidden Markov models; Image analysis; Image motion analysis; Image reconstruction; Image sequence analysis; Laboratories
• DOI: 10.1109/EURCON.2003.1248176

We present a system for tracking laboratory animals during pharmacological experiments. As it is usually possible to ensure good contrast between the animals and the background, tracking of a single animal or several physically separated animals can be achieved by relatively simple algorithms. The main problem arises when we try to track several almost identical, uniformly coloured animals during their contacts. To deal with this problem we represent objects by parametrically deformable contour models. The model has been built by observing videos containing a single animal (a laboratory mouse). To reflect symmetry, the model is axial and contains the offsets of the contour segments from the axis of minimal inertia. The deformation is modeled as stretching and bending. The tracking is done in two steps. For the tracking of objects from frame to frame we use the rigidity assumption, i.e. in the first step the contour models which represent objects in the previous frame are translated into new positions. In the second step the object position, rotation and scale, as well as the deformation parameters, are fine-tuned to match the object boundaries. The interframe translation is estimated by minimizing the sum of squared differences (SDD) over the search window for all tracked contour points. The model fitting is based on maximizing the contour energy in terms of the underlying smoothed gradient image. The robustness of the tracking algorithm is improved by adding a supervision module, which detects tracking failures and reinitializes the contours that lose their targets. The system has been tested on real sequences with laboratory animals during pharmacological experiments and has been shown to be robust and efficient. Future extensions will include expert knowledge of biomedical and pharmacological experts. The major goal is to build a system that will provide a tool for objective evaluation of animal behaviour during experiments.