Spiking Neural Network based Region Proposal Networks for Neuromorphic Vision Sensors

被引：0
|
作者：
Acharya, Jyotibdha [1 ]
Padala, Vandana [2 ]
Basu, Arindam [2 ]
机构：
[1] Nanyang Technol Univ, Interdisciplinary Grad Sch, HealthTech NTU, Singapore, Singapore
[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
来源：
2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | 2019年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents a three layer spiking neural network based region proposal network operating on data generated by neuromorphic vision sensors. The proposed architecture consists of refractory, convolution and clustering layers designed with bio-realistic leaky integrate and fire (LIF) neurons and synapses. The proposed algorithm is tested on traffic scene recordings from a DAVIS sensor setup. The performance of the region proposal network has been compared with event based mean shift algorithm and is found to be far superior (approximate to 50% better) in recall for similar precision (approximate to 85%). Computational and memory complexity of the proposed method are also shown to be similar to that of event based mean shift.
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页数：5
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