Efficient and Low-Footprint Object Classification using Spatial Contrast
Matthew Belding Daniel C. Stumpp Rajkumar Kubendran
Event-based vision sensors traditionally compute temporal contrast that
offers potential for low-power and low-latency sensing and computing. In this
research, an alternative paradigm for event-based sensors using localized
spatial contrast (SC) under two different thresholding techniques, relative and
absolute, is investigated. Given the slow maturity of spatial contrast in
comparison to temporal-based sensors, a theoretical simulated output of such a
hardware sensor is explored. Furthermore, we evaluate traffic sign
classification using the German Traffic Sign dataset (GTSRB) with well-known
Deep Neural Networks (DNNs). This study shows that spatial contrast can
effectively capture salient image features needed for classification using a
Binarized DNN with significant reduction in input data usage (at least 12X) and
memory resources (17.5X), compared to high precision RGB images and DNN, with
only a small loss (~2%) in macro F1-score. Binarized MicronNet achieves an
F1-score of 94.4% using spatial contrast, compared to only 56.3% when using RGB
input images. Thus, SC offers great promise for deployment in power and
resource constrained edge computing environments.