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Applications in the Internet of Video Things (IoVT) domain have very tightconstraints with respect to power and area. While neuromorphic vision sensors(NVS) may offer advantages over traditional imagers in this domain, theexisting NVS systems either do not meet the power constraints or have notdemonstrated end-to-end system performance. To address this, we improve on arecently proposed hybrid event-frame approach by using morphological imageprocessing algorithms for region proposal and address the low-power requirementfor object detection and classification by exploring various convolutionalneural network (CNN) architectures. Specifically, we compare the resultsobtained from our object detection framework against the state-of-the-artlow-power NVS surveillance system and show an improved accuracy of 82.16% from63.1%. Moreover, we show that using multiple bits does not improve accuracy,and thus, system designers can save power and area by using only single bitevent polarity information. In addition, we explore the CNN architecture spacefor object classification and show useful insights to trade-off accuracy forlower power using lesser memory and arithmetic operations.

HyNNA: Improved Performance for Neuromorphic Vision Sensor based Surveillance using Hybrid Neural Network Architecture