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 interaction...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 21; no. 9; pp. 3990 - 3999
Main Authors: Ludl, Dennis, Gulde, Thomas, Curio, Cristobal
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
autonomous vehicles
Avatars
classification algorithms
Computational modeling
Computer simulation
computer vision
Data models
Datasets
Ground truth
Intelligent transportation systems
machine learning
Motion capture
Motion perception
Pose estimation
Recognition
Recurrent neural networks
Simulation
Solid modeling
Three dimensional motion
Three-dimensional displays
Training data
Urban areas
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary: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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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2020.2988504