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Progression Modelling for Online and Early Gesture Detection

This paper discusses the goals, approaches, and results of online and timely gesture detection for improved user experience. It introduces a light-weight, explainable, online gesture recognition framework with annotations that will be publicly released. The framework allows for better control over downstream tasks and can be customized with different strategies for online gesture triggering.

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Progression Modelling for Online and Early Gesture Detection

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  1. Progression Modelling for Online and Early Gesture Detection Sai Kumar Dwivedi Mercedes-Benz R&D India Rishabh Dabral IIT Bombay Arjun Jain IIT Bombay, Axogyan AI Vikram Gupta Mercedes-Benz R&D India

  2. Gestures are Everywhere

  3. Goals • Online and timely gesture detection for improved UX (Mitraet al.) • Model the stageof gesture progression for better control over downstream tasks. • Improved gesture detection

  4. Possible approaches Connectionist Temporal Classification (Molchanovet al.) Gesture Localization (Pigou et al.,Neverovaet al.) Gesture Progression

  5. Proposal Global Temporal Features Local Spatio-Temporal Features • No temporal pooling in 3DCNN • allows for fine-grained gesture detection with minimal lag.

  6. Neo-NVIDIA annotations • We introduce tighter annotations for NVIDIA hand gesture dataset, Molchanovet al. NVIDIA annotations Neo-NVIDIA annotations

  7. Inference:Online vs Offline Offline Trigger Online Trigger GPM Threshold Gesture Progression Level Time Can have multiple strategies for online gesture trigger!

  8. Results Error in detection

  9. Results Comparison of classification accuracy (%) between choices of backbone network on depth modality. Comparison of classification accuracy (%) in offline setting on the NVIDIA gesture dataset.

  10. Analysis of GPM Accuracy at multiple percentages of gesture progression vis-à-vis the max gesture progression level. False Positive Rate decreases as the trigger threshold increases.

  11. Conclusion and Future Work • A light-weight, explainable, online gesture recognition framework. • The framework provides downstream use-cases adequate control over the system behaviour. • Annotations will be publicly released. • Can the linear progression assumption be improved? Queries? Please contact Vikram Gupta vikram.gupta@daimler.com

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