Corrigendum: SCTN: event-based object tracking with energy-efficient deep convolutional spiking neural networks

Mingcheng Ji; Ziling Wang; Rui Yan; Qingjie Liu; Shu Xu; Huajin Tang

doi:10.3389/fnins.2023.1204334

Corrigendum: SCTN: event-based object tracking with energy-efficient deep convolutional spiking neural networks

Front Neurosci. 2023 May 16:17:1204334. doi: 10.3389/fnins.2023.1204334. eCollection 2023.

Authors

Mingcheng Ji¹, Ziling Wang¹, Rui Yan², Qingjie Liu³, Shu Xu³, Huajin Tang^{1

4}

Affiliations

¹ College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
² College of Computer Science, Zhejiang University of Technology, Hangzhou, China.
³ Machine Intelligence Laboratory, China Nanhu Academy of Electronics and Information Technology, Jiaxing, China.
⁴ Zhejiang Lab, Hangzhou, China.

Abstract

[This corrects the article DOI: 10.3389/fnins.2023.1123698.].

Keywords: event cameras; event-based tracking dataset; exponential IoU; object tracking; spiking neural networks.

Publication types

Published Erratum

Grants and funding

This work was supported by the Key Research Project of Zhejiang Lab under Grant: 2021KC0AC01 and the National Natural Science Foundation of China under Grant: 62236007.