Skip to main content
Autonomous Fish Tracking by ROV Using Monocular Camera
Proceedings of the 3rd Canadian Conference on Computer and Robot Vision
  • Jun Zhou, University of Waterloo
  • Christopher M. Clark, University of Waterloo
Publication Date

This paper concerns the autonomous tracking of fish using a Remotely Operated Vehicle (ROV) equipped with a single camera. An efficient image processing algorithm is presented that enables pose estimation of a particular species of fish - a Large Mouth Bass. The algorithm uses a series of filters including the Gabor filter for texture, projection segmentation, and geometrical shape feature extraction to find the fishes distinctive dark lines that mark the body and tail. Feature based scaling then produces the position and orientation of the fish relative to the ROV. By implementing this algorithm on each frame of a series of video frames, successive relative state estimates can be obtained which are fused across time via a Kalman Filter. Video taken from a VideoRay MicroROV operating within Paradise Lake, Ontario, Canada was used to demonstrate off-line fish state estimation. In the future, this approach will be integrated within a closed-loop controller that allows the robot to autonomously follow the fish and monitor its behavior.

Number of Pages
Publisher statement
Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Citation Information
Jun Zhou and Christopher M. Clark. "Autonomous Fish Tracking by ROV Using Monocular Camera" Proceedings of the 3rd Canadian Conference on Computer and Robot Vision (2006)
Available at: