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Enhancing data-driven algorithms for human pose estimation and action recognition through simulation

  • Recognizing human actions, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Intelligent transport systems in particular face this challenge, as interactions with people are often required. The development and testing of technical perception solutions is done mostly on standard vision benchmark datasets for which manual labelling of sensory ground truth has been a tedious but necessary task. Furthermore, rarely occurring human activities are underrepresented in these datasets, leading to algorithms not recognizing such activities. For this purpose, we introduce a modular simulation framework, which offers to train and validate algorithms on various human-centred scenarios. We describe the usage of simulation data to train a state-of-the-art human pose estimation algorithm to recognize unusual human activities in urban areas. Since the recognition of human actions can be an important component of intelligent transport systems, we investigated how simulations can be applied for his purpose. Laboratory experiments show that we can train a recurrent neural network with only simulated data based on motion capture data and 3D avatars, which achieves an almost perfect performance in the classification of those human actions on real data.

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Metadaten
Author of HS ReutlingenLudl, Dennis; Gulde, Thomas; Curio, Cristóbal
DOI:https://doi.org/10.1109/TITS.2020.2988504
ISSN:1524-9050
eISSN:1558-0016
Erschienen in:IEEE transactions on intelligent transportation systems
Publisher:Inst. of Electrical and Electronics Engineers
Place of publication:New York
Document Type:Article
Language:English
Year of Publication:2020
Tag:autonomous vehicles; classification algorithms; computer vision; machine learning; recurrent neural networks; simulation
Volume:21
Issue:9
Page Number:10
First Page:3990
Last Page:3999
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Open Access?:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE