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

@article{Singla2020HyNNAIP,
  title={HyNNA: Improved Performance for Neuromorphic Vision Sensor Based Surveillance using Hybrid Neural Network Architecture},
  author={Deepak Singla and Soham Chatterjee and Lavanya Ramapantulu and Andr{\'e}s Ussa and Bharath Ramesh and Arindam Basu},
  journal={2020 IEEE International Symposium on Circuits and Systems (ISCAS)},
  year={2020},
  pages={1-5}
}
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… Expand
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