Quantification of cyclic motion of marine animals from computer vision

Abstract

A vision-based automatic tracking system mounted on the ROV Ventana has successfully tracked jellyfish in Monterey Bay, California as part of a joint project between Stanford University and the Monterey Bay Aquarium Research Institute (MBARI). To enhance performance, improved lead information about the target's motion is desired. Human pilots derive lead information about that motion through the perception of motion modes of these animals and the propulsion associated with them. Although this kind of information is of a different nature than what is typically available to an automatic control system, performance gains could be achieved by incorporation of such information. Jellyfish and related gelatinous animals exhibit very distinct modes of motion that are visually recognizable to human observers. For an autonomous animal-tracking system to interpret the motion of its target, and to generate lead information useful for control, it must first be able to identify the mode motion of the animal under observation. This paper explores techniques in computer vision to detect and recognize the key motion modes and mode change events typical of these animals. Methods are presented to distinguish between active and resting modes, and to detect and monitor rhythmic patterns in the body motions of these animals.