TY - GEN
T1 - Temporal Up-Sampling for Asynchronous Events
AU - Xiang, Xijie
AU - Zhu, Lin
AU - Li, Jianing
AU - Tian, Yonghong
AU - Huang, Tiejun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid events directly affects the performance of event-based tasks, such as reconstruction, detection, and recognition. However, when in low-brightness or slow-moving scenes, events are often sparse and accompanied by noise, which poses challenges for event-based tasks. To solve these challenges, we propose an event temporal up-sampling algorithm 11Code: https://github.com/XIJIE-XIANG/Event-Temporal-Up-sampling to generate more effective and reliable events. The main idea of our algorithm is to generate up-sampling events on the event motion trajectory. First, we estimate the event motion trajectory by contrast maximization algorithm and then up-sampling the events by temporal point processes. Experimental results show that up-sampling events can provide more effective information and improve the performance of downstream tasks, such as improving the quality of reconstructed images and increasing the accuracy of object detection.
AB - The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid events directly affects the performance of event-based tasks, such as reconstruction, detection, and recognition. However, when in low-brightness or slow-moving scenes, events are often sparse and accompanied by noise, which poses challenges for event-based tasks. To solve these challenges, we propose an event temporal up-sampling algorithm 11Code: https://github.com/XIJIE-XIANG/Event-Temporal-Up-sampling to generate more effective and reliable events. The main idea of our algorithm is to generate up-sampling events on the event motion trajectory. First, we estimate the event motion trajectory by contrast maximization algorithm and then up-sampling the events by temporal point processes. Experimental results show that up-sampling events can provide more effective information and improve the performance of downstream tasks, such as improving the quality of reconstructed images and increasing the accuracy of object detection.
KW - Temporal up-sampling
KW - contrast maximization
KW - event camera
KW - temporal point process
UR - http://www.scopus.com/inward/record.url?scp=85137666026&partnerID=8YFLogxK
U2 - 10.1109/ICME52920.2022.9858934
DO - 10.1109/ICME52920.2022.9858934
M3 - Conference contribution
AN - SCOPUS:85137666026
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - ICME 2022 - IEEE International Conference on Multimedia and Expo 2022, Proceedings
PB - IEEE Computer Society
T2 - 2022 IEEE International Conference on Multimedia and Expo, ICME 2022
Y2 - 18 July 2022 through 22 July 2022
ER -