Making Sense of Neuromorphic Event Data for Human Action Recognition
Neuromorphic vision sensors provide low power sensing and capture salient spatial-temporal events. The majority of the existing neuromorphic sensing work focus on object detection. However, since they only record the events, they provide an efficient signal domain for privacy aware surveillance task...
Main Authors: | Salah Al-Obaidi, Hiba Al-Khafaji, Charith Abhayaratne |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9446050/ |
Similar Items
-
Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud Compression
by: Jayasingam Adhuran, et al.
Published: (2024-02-01) -
Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation
by: Bowen Du, et al.
Published: (2021-06-01) -
Benchmarking Neuromorphic Vision: Lessons Learnt from Computer Vision
by: Cheston eTan, et al.
Published: (2015-10-01) -
Lossless Compression of Neuromorphic Vision Sensor Data Based on Point Cloud Representation
by: Maria Martini, et al.
Published: (2022-01-01) -
Neuromorphic Eye-in-Hand Visual Servoing
by: Rajkumar Muthusamy, et al.
Published: (2021-01-01)