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Courtesy of Chop & Learn team

To develop the datasets, Saini and fellow computer science doctoral students Hanyu Wang and Archana Swaminathan filmed themselves chopping 20 types of fruits and vegetables in seven styles using video cameras set up at four angles.

The variety of angles, people and food-prepping styles are necessary for a comprehensive data set, said Saini.

Someone may peel their apple or potato before chopping it, while other people dont. The computer is going to recognize that differently, she said.

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In addition to Saini, Wang and Swaminathan, the Chop & Learn team includes computer science doctoral students Vinoj Jayasundara and Bo He; Kamal Gupta Ph.D. 23, now at Tesla Optimus; and their adviser Abhinav Shrivastava, an assistant professor of computer science.

Being able to recognize objects as they are undergoing different transformations is crucial for building long-term video understanding systems, said Shrivastava, who also has an appointment in the University of Maryland Institute for Advanced Computer Studies. We believe our dataset is a good start to making real progress on the basic crux of this problem.

In the short term, Shrivastava said, the Chop & Learn dataset will contribute to the advancement of image and video tasks such as 3D reconstruction, video generation, and summarization and parsing of long-term video.

Those advances could one day have a broader impact on applications like safety features in driverless vehicles or helping officials identify public safety threats, he said.

And while its not the immediate goal, Shrivastava said, Chop & Learn could contribute to the development of a robotic chef that could turn produce into healthy meals in your kitchen on command.

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Maryland Today | Can Chopping Fruit Help Computers Learn? - Maryland Today