Underwater swimmer segmentation

An unsupervised approach to segment the swimmer in hydrodynamic video sequences is presented in this paper. Detecting and segmenting objects in hydrodynamic scenes is challenging as both the desired foreground and unwanted background are driven by complex nonlinear dynamics. These dynamics typically comprise a chaotic mix of vortices, flows and turbulence. Medium introduced reflection and light diffraction creates the camouflage, rendering the swimmer detection even more difficult. On the other hand, spatial regularities and temporal distortions within these sequences often exhibit signature-like patterns. The novelty of our approach is to combine texture modeling and photometric features in order to identify the foreground subject, which lacks the visual characteristics of hydrodynamic content. Segmentation results are then temporally propagated from one frame to another to reduce the effects of camouflage and confine the segmentation to desired region. Simulation results demonstrate ability of the algorithm to separate human body in hydrodynamic sequences for silhouette based underwater motion capture and analysis.