Mr. Ashwin Ramachandran Profile

Ashwin Ramachandran

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Proceedings Article | 10 April 2023 Poster + Paper

Video-based in-vehicle action recognition for continuous health monitoring

Ashwin Ramachandran, Kartik Gokhale, Maike Kripps, Thomas Deserno

Proceedings Volume 12469, 124690V (2023) https://doi.org/10.1117/12.2655116

KEYWORDS: Video, Pose estimation, Action recognition, Histograms, Visual process modeling, Feature extraction, Motion models, 3D modeling, RGB color model, Data modeling

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Driver action recognition is essential in vehicle safety and smart car systems targeting vehicles as a diagnostic space. In the context of video-based action recognition, attention-based architectures have surpassed conventional methods in deep learning. Former frameworks have produced excellent results on the public Drive&Act dataset. However, present frameworks do not consider the temporal ordering of frames in the video and the spatial layout of the relevant interacting objects yielding poor performance in certain actions that include semantic reversals. This includes so-called conjugate actions that originate when performed backward in time. An example would be moving the hand rightward vs. leftward. We propose a feature engineering approach to model the motion of human pose. We use key points relevant to the action to incorporate the sequential order. We implement video swin architecture on the Drive&Act dataset. Then, we utilize the histogram of oriented displacements on human joint locations and their displacements and train a support vector machine to classify actions in conjugate pairs. Performance increases in two conjugate actions namely fastening/ unfastening seat belt and taking off/ putting on sunglasses. Integrating our module with existing deep learning models increases the overall accuracy by 3% to 72%. Furthermore, our approach can be extended to other action classes.

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