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About Ng Lee Ping

Ng Lee Ping

Ng Lee Ping

I'm the lead engineer for Computer Vision in AI Singapore. I like designing things, building LEGO, and writing code in my spare time.
AI Bricks/Products

PeekingDuck V1.2 – A Major Update!

The PeekingDuck team is excited to share our latest v1.2 release! Our model zoo has been expanded with new object tracking, crowd counting, object detection, and pose estimation model nodes. We have also revamped our documentation by making information easier to find, and adding tutorials to guide users. Useful nodes such as for aggregating statistics

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AI Bricks/Products

Privacy Protection Using Peekingduck

”Data is the new oil” is a catchphrase that is reflective of modern times. As more data is being collected and shared, data privacy and protection becomes increasingly important, and governments around the world have been implementing regulations in response. In the European Union, the General Data Protection Regulation (GDPR) was adopted in 2016, while

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AI Bricks/Products

PeekingDuck: A Computer Vision Framework

AI Singapore’s Computer Vision (CV) Hub has worked on several industry projects over the last few years. We found that while the types of CV problems are very varied, many of these projects share repeated code, such as reading from a video or image. In some projects, even pre-trained CV models for object detection and

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AI Bricks/Products

COVID-19: Stay Vigilant With Group Size Checker

In tandem with the rise in COVID-19 cases in the community, the Singapore Government has been progressively tightening restrictions on the group sizes of social gatherings. Group size limits were reduced from 8 to 5 on May 8 (reversion to Phase 2), and further from 5 to 2 on May 16 (Phase 2 – Heightened

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AI Bricks/Products

Introduction to Computer Vision (CV) Hub

Since its establishment in 2017, AI Singapore (AISG) has embarked on numerous Computer Vision projects within the 100E programme, which helps organisations use AI to solve their business problems. One such collaboration was with IBM in which AISG delivered a system to help their product engineers better predict the return rates of different hardware products (check

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