Real Time Gesture to Automotive Control

Chaman, Shilpa; Jani, Jay; Fernandes, Henson; Dhuka, Rahila; Mehta, Dhanvin

doi:10.1109/icctct.2018.8550879

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Finally, we demonstrate real-time performance in our autonomous vehicle. Our code and pre-trained models are publicly available 1 .…”

Section: Gesturementioning

confidence: 99%

“…pointing to a desired parking spot. Because of its high importance, the problem of gesture recognition for autonomous vehicles is not new to the community [1], [2], [3]. The current state-of-the-art for indoor [4] and outdoor [5] gesture recognition builds on deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…Part of the work was supported by INTUITIVER (7547.223-3/4/), funded by State Ministry of Baden-Württemberg for Sciences, Research and Arts and the State Ministry of Transport Baden-Württemberg, as well as, the LUKAS project (19A20004F), funded by German Federal Ministry for Economic Affairs and Energy (BMWi). 1 Institute of Measurement, Control and Microtechnology, Ulm University, Albert-Einstein-Allee 41, 89081 Ulm, Germany.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Spatio-Temporal Multilayer Perceptron for Gesture Recognition

Holzbock,

Tsaregorodtsev,

Dawoud

et al. 2022

Preprint

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Gesture recognition is essential for the interaction of autonomous vehicles with humans. While the current approaches focus on combining several modalities like image features, keypoints and bone vectors, we present neural network architecture that delivers state-of-the-art results only with body skeleton input data. We propose the spatio-temporal multilayer perceptron for gesture recognition in the context of autonomous vehicles. Given 3D body poses over time, we define temporal and spatial mixing operations to extract features in both domains. Additionally, the importance of each time step is re-weighted with Squeeze-and-Excitation layers. An extensive evaluation of the TCG and Drive&Act datasets is provided to showcase the promising performance of our approach. Furthermore, we deploy our model to our autonomous vehicle to show its realtime capability and stable execution.

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“…Finally, we demonstrate real-time performance in our autonomous vehicle. Our code and pre-trained models are publicly available 1 .…”

Section: Gesturementioning

confidence: 99%