Yizhou Jiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yizhou Jiang has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Chu He
Electronic Information School, Wuhan University, Wuhan 430072, China
2 shared publications - Lei Yu
School of Artificial Intelligence, Wuhan University, Wuhan, China
2 shared publications - Wenwei Wang
School of Electronic Information, Wuhan University, Wuhan 430072, China
2 shared publications
Latest Publications
- Open Access
- 22 July 2024
- Published by MDPI AG in Electronics
- Vol. 13 (14) , 2879
Eye Tracking Based on Event Camera and Spiking Neural Network
An event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, and addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye-tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of a spiking neural network (SNN) with an event camera, a near-range eye-tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081 ms, enhancing tracking stability while maintaining accuracy.
- 22 July 2024
- Published by MDPI AG in Electronics
- Vol. 13 (14) , 2879
Open AccessEye Tracking Based on Event Camera and Spiking Neural Network
An event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, and addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye-tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of a spiking neural network (SNN) with an event camera, a near-range eye-tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081 ms, enhancing tracking stability while maintaining accuracy.
- Open Access
- 24 May 2024
- Published by MDPI AG in Preprints.org
Eye Tracking Based on Event Camera and Spiking Neural Network
The event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of spiking neural network (SNN) with event camera, a near-range eye tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5\% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081ms, enhancing tracking stability while maintaining accuracy.
- 24 May 2024
- Published by MDPI AG in Preprints.org
Open AccessEye Tracking Based on Event Camera and Spiking Neural Network
The event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of spiking neural network (SNN) with event camera, a near-range eye tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5\% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081ms, enhancing tracking stability while maintaining accuracy.