Extracting semantic information from point-wise observations at scale is a complex problem usually solved by using traditional brute force spatial analysis techniques. These techniques employ rigid rule-based approaches that, though somewhat accurate, fail to understand cases outside the given rule set and are computationally expensive. To address these shortcomings, CCRi has explores new ways to efficiently analyze tracks by encoding spatiotemporal components in a joint representation in the video domain. In this presentation, we will show the usefulness of convolutional feature extraction compared to traditional kinematic analysis practices, and demonstrate extraction of behavioral information for maritime vessels in large regions in an effort to detect illicit activities. An example of such activities is the monitoring of fishing transhipment behaviors, where smaller fishing vessels off-load their catch to large reefer vessels to maximize fishing yield in international waters, severely depleting natural fishing grounds. The presentation will include a collection of animated visualizations to illustrate the aforementioned solutions.
Data Scientist, Commonwealth Computer Research, Inc. (CCRi)
Martin is a data scientist at Commonwealth Computer Research. His most recent work involves using computer vision and deep learning to localize targeted behaviors from maritime vessel trajectory data. He recently graduated from the University of Virginia with a bachelors in mathematical... Read More →
