Using CSNNs to Perform Event-based Data Processing & Classification on ASL-DVS
This work addresses the challenge of handling asynchronous and sparse event-based sequences for sign language recognition, but it appears incremental as it applies an existing CSNN method to a specific dataset.
The paper tackled the problem of processing and classifying event-based data from the ASL-DVS gesture dataset using a convolutional spiking neural network (CSNN), achieving 100% training accuracy on a pre-processed subset.
Recent advancements in bio-inspired visual sensing and neuromorphic computing have led to the development of various highly efficient bio-inspired solutions with real-world applications. One notable application integrates event-based cameras with spiking neural networks (SNNs) to process event-based sequences that are asynchronous and sparse, making them difficult to handle. In this project, we develop a convolutional spiking neural network (CSNN) architecture that leverages convolutional operations and recurrent properties of a spiking neuron to learn the spatial and temporal relations in the ASL-DVS gesture dataset. The ASL-DVS gesture dataset is a neuromorphic dataset containing hand gestures when displaying 24 letters (A to Y, excluding J and Z due to the nature of their symbols) from the American Sign Language (ASL). We performed classification on a pre-processed subset of the full ASL-DVS dataset to identify letter signs and achieved 100\% training accuracy. Specifically, this was achieved by training in the Google Cloud compute platform while using a learning rate of 0.0005, batch size of 25 (total of 20 batches), 200 iterations, and 10 epochs.