A posture optimization algorithm for model-based motion capture of movement sequences

Abstract

We have developed and evaluated a new optical motion capture approach that is suitable for a wide range of studies in neuroethology and motor control. Based on the stochastic search algorithm of Simulated Annealing (SA), it utilizes a kinematic body model that includes joint angle constraints to reconstruct posture from an arbitrary number of views. Rather than tracking marker trajectories in time, the algorithm minimizes an error function that compares predicted model projections to the recorded views. Thus, each video-frame is analyzed independently from other frames, enabling the system to recover from incorrectly analyzed postures. The system works with standard computer and video equipment. Its accuracy is evaluated using videos of animated locust leg movements, recorded by two orthogonal views. The resulting joint angle RMS errors range between 0.7° and 4.9°, limited by the pixel resolution of the digital video. 3D-movement reconstruction is possible even from a single view. In a real experimental application, stick insect walking sequences are analyzed with leg joint angle deviations between 0.5° and 3.0°. This robust and accurate performance is reached in spite of marker fusions and occlusions, simply by exploiting the natural contraints imposed by a kinematic chain and a known experimental setup.