Graph-Based Object Classification for Neuromorphic Vision Sensing

Abstract

Neuromorphic vision sensing (NVS)\ devices represent visual information as sequences of asynchronous discrete events (a.k.a., ``spikes'') in response to changes in scene reflectance. Unlike conventional active pixel sensing (APS), NVS allows for significantly higher event sampling rates at substantially increased energy efficiency and robustness to illumination changes. However, object classification with NVS streams cannot leverage on state-of-the-art convolutional neural networks (CNNs), since NVS does not produce frame representations. To circumvent this mismatch between sensing and processing with CNNs, we propose a compact graph representation for NVS. We couple this with novel residual graph CNN architectures and show that, when trained on spatio-temporal NVS data for object classification, such residual graph CNNs preserve the spatial and temporal coherence of spike events, while requiring less computation and memory. Finally, to address the absence of large real-world NVS datasets for complex recognition tasks, we present and make available a 100k dataset of NVS recordings of the American sign language letters, acquired with an iniLabs DAVIS240c device under real-world conditions.

13 pages, 4 figures, ICCV 2019

References in corpus (10)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Spectral Networks and Locally Connected Networks on Graphs
• Discrete Signal Processing on Graphs
• EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
• Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
• Learning shape correspondence with anisotropic convolutional neural networks
• Local Spectral Graph Convolution for Point Set Feature Learning
• Attention Mechanisms for Object Recognition with Event-Based Cameras