December 2020 OES Beacon

Shore-based monitoring and quantification of vessel activities: University of Victoria Photographic Observation Study (POS).

Ben Morrow, M.Sc. candidate/OES student member

Photobot diagram

With the support of IEEE OES Victoria Chapter, Ben Morrow, alongside researchers Norma Serra and Dr. Patrick O’Hara have developed and installed three remote shore-based passive systems for the monitoring of nearshore regions to capture marine vessel traffic and marine mammal presence and activities around Sooke, BC, in western Canada.

These systems consist of cameras and in-situ automatic identification systems (AIS). The initial “photobot” was developed in 2019 and several iterations have occurred to improve the systems. At present, as seen in the diagram, they consist of two cameras, (Lorex PTZ and Canon SLR), a digital yacht AIS receiver with Shakespeare antennas, web-controlled power bars for on/off capabilities calibrated to sunrise and sunset, which are controlled remotely with raspberry pi’s (via Dataplicity). Each system accesses internet either via a LAN or LTE router and draws power either directly through 120V or remotely through 12V rechargeable deep-cycle batteries.

Researchers Ben Morrow and Dr. Patrick O’Hara installing a photobot for passive coastal monitoring on top of the Sheringham Point lighthouse, west of Sooke, BC.

“Photobots” are made from durable PVC watertight cases with rubber gaskets, have precise windows cut out and glassed over to fit camera lenses, and are fitted with sunshades and polarizing filters for glare reduction. Each camera continuously takes approximately 2600 photos/day, with data being collected 365 days per year at each site. Due to the enormous data volume, a number of processing tools were developed by Ph.D. candidate Tunai Marquez and undergraduate student Gregory O’Hagan to speed up processing times, in order to serve the particular research questions. Various tools have been created in Python and MATLAB including one that links AIS identifications to images using timestamps and positions calibrated in camera’s fields-of-view, one that allows for the semi-automatic annotation of types of marine vessels, and deep learning algorithms for the passive detection of vessels.

The team would like to thank IEEE OES Victoria Chapter for their generous contribution for the technology and supporting hardware in making this project feasible. The POS project’s aims are to develop and utilize tools to observe and analyze marine vessel activities, marine fauna, and their interactions, in order to inform policy and management. For more information, please visit the Photographic Observation Study website at