  Applications in the Internet of Video Things (IoVT) domain have very tight
constraints with respect to power and area. While neuromorphic vision sensors
(NVS) may offer advantages over traditional imagers in this domain, the
existing NVS systems either do not meet the power constraints or have not
demonstrated end-to-end system performance. To address this, we improve on a
recently proposed hybrid event-frame approach by using morphological image
processing algorithms for region proposal and address the low-power requirement
for object detection and classification by exploring various convolutional
neural network (CNN) architectures. Specifically, we compare the results
obtained from our object detection framework against the state-of-the-art
low-power NVS surveillance system and show an improved accuracy of 82.16% from
63.1%. Moreover, we show that using multiple bits does not improve accuracy,
and thus, system designers can save power and area by using only single bit
event polarity information. In addition, we explore the CNN architecture space
for object classification and show useful insights to trade-off accuracy for
lower power using lesser memory and arithmetic operations.
