Brain-inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform

Brain-inspired computer vision (BICV) has evolved rapidly in recent years and it is now competitive with traditional CV approaches. However, most of BICV algorithms have been developed on high power-and-performance platforms (e.g. workstations) or special purpose hardware. We propose two different algorithms for counting people in a classroom, both based on Convolutional Neural Networks (CNNs), a state-of-art deep learning model that is inspired on the structure of the human visual cortex. Furthermore, we provide a standalone parallel C-library that implements CNNs and use it to deploy our algorithms on the embedded mobile ARM big.LITTLE-based Odroid-XU platform. Our performance and power measurements show that neuromorphic vision is feasible on off-the-shelf embedded mobile platforms, and we show that it can reach very good energy efficiency for non-time-critical tasks such as people counting.

Francesco Conti, Antonio Pullini and Luca Benini. Brain-inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 624-629, 28 June 2014. DOI: 10.1109/CVPRW.2014.95


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