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SynDa: A Novel Synthetic Data Generation Pipeline for Activity Recognition

conference contribution
posted on 2023-07-03, 08:49 authored by Megani Rajendran, Chek Tien TanChek Tien Tan, Indriyati AtmosukartoIndriyati Atmosukarto, Aik Beng Ng, Simon See

The task of classifying or predicting the activity performed by humans is called human activity recognition. Many existing models aim to solve the problem of activity recognition in this field, but we recognise the lack of real data can have a great impact on the effectiveness of such models. In this paper, we introduce SynDa, a first-of-its-kind streamlined semi-automated pipeline for synthetic data generation built using photorealistic rendering and AI pose estimation, that harvests existing real-life video datasets to create new large-scale datasets. The synthetic data can augment real data to train models robustly and overcome the lack of and associated costs to acquire more real data. Preliminary work indicates that combining real data and synthetic video data generated using this pipeline to train models presents a mAP of 32.35%, while a model trained on real data presented 29.95%. 

History

Journal/Conference/Book title

2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

Publication date

2022-12-15

Project ID

  • 9183 Recognizing Temporal State Changes of Spatial Objects Over Time in Videos for Digital Assistants in Everyday Living

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