A Low Power, Fully Event-Based Gesture Recognition System

By A. Amir, Brian Taba, David J. Berg, T. Melano, Jeffrey L. McKinstry, C. D. Nolfo, T. Nayak, Alexander Andreopoulos, Guillaume J. Garreau, Marcela Mendoza and 6 more. Computer Vision and Pattern Recognition, 2017

1,089 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/1e5f6ff1971072530a8bc50036d7de5fc3e80f67

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Citations in the corpus

Attention Spiking Neural Networks (2022, neutral): “A wide range of experiments show the effectiveness and efficiency of MA, which achieve state-of-the-art performance and significant energy efficiency across multiple datasets and tasks, including event-based DVS128 Gesture/Gait and ImageNet-1 K.”
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras (2018, background): “[1], where a network is adapted to the TrueNorth chip to perform gesture recognition.”
ESIM: an Open Event Camera Simulator (2018, background): “Most of these datasets target the task of object recognition [16, 17, 18], or gesture recognition [19].”
Spikformer: When Spiking Neural Network Meets Transformer (2022, methodology): “We conduct experiments on both static datasets CIFAR, ImageNet (Deng et al., 2009), and neuro-morphic datasets CIFAR10-DVS, DVS128 Gesture (Amir et al., 2017) to evaluate the performance of Spikformer.”
Deep Residual Learning in Spiking Neural Networks (2021, methodology): “We evaluate Spiking ResNet and SEW ResNet on both the static ImageNet dataset and the neuromorphic DVS Gesture dataset [1], CIFAR10-DVS dataset [32].”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “Recently, event-based sensors and neuromorphic processors have been proposed [124].”
Spike-driven Transformer (2023, methodology): “The latter contains CIFAR10-DVS [78] and DVS128 Gesture [79].”
Neural heterogeneity promotes robust learning (2020, background): “2017, and the DVS128 Gesture dataset (Amir et al., 2017) feature visual stimuli, while the Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC) datasets (Cramer et al.”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, methodology): “…event representation (AER) protocol (Mahowald, 1994; Boahen, 2000), which is used to connect, e.g., event-based sensors (Lichtsteiner et al., 2008) via digital interconnect to neuromorphic chips (Indiveri et al., 2011; Amir et al., 2017) or digital post-processing hardware (Furber et al., 2014).”
Training Spiking Neural Networks Using Lessons From Deep Learning (2021, methodology): “Cross entropy minimisation is used to increase the spike count of the correct class, while suppressing the count of the incorrect classes [99,117] (Appendix B.”
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE) (2018, methodology): “Learning was performed on the dataset provided with [1] and consists of 11 gestures.”
Learning to Detect Objects with a 1 Megapixel Event Camera (2020, background): “Despite the increasing effort of the community [29, 30, 31, 32], very few datasets provide ground-truth for object detection.”
Opportunities for neuromorphic computing algorithms and applications (2022, background): “Several groups have created datasets with event/spike-based representation and temporal dimension specifically for benchmarking neuro-morphic training algorithms, such as the neuromorphic MNIST 112 , DVS Gesture 9 and the Spiking Heidelberg audio datasets 41 ; however, these datasets have not yet be”
End-to-End Learning of Representations for Asynchronous Event-Based Data (2019, methodology): “Spiking Neural Networks (SNNs) are a natural ﬁt to process event streams, since they enable asynchronous inference at low power on specialized hardware [2, 32, 47].”
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks (2019, methodology): “More recently, IBM has released the DVS128 Gesture Data set [42] under a Creative Commons license.”