Unlike traditional SLT based on visible light videos, which is easily affected by factors such as lighting, rapid hand movements, and privacy breaches, this paper proposes the use of high-definition Event streams for SLT, effectively mitigating the aforementioned issues.
This paper proposes a novel hierarchical knowledge distillation framework that can fully utilize multi-modal / multi-view information during training to facilitate knowledge transfer, enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals.
This paper introduces a new hybrid dataset encompassing both RGB and event data for human pose estimation and tracking in two extreme scenarios: low-light and motion blur environments.
This paper proposes to recognize the scene text using bio-inspired event cameras by collecting and annotating a large-scale benchmark dataset, termed EventSTR. It contains 9,928 high-definition (1280 × 720) event samples and involves both Chinese and English characters.
This paper talks about the mission design and objectives for Falcon ODIN along with ground-based testing of all four camera. Falcon ODIN contains two event based cameras (EBC) and two traditional framing cameras along with mirrors mounted on azimuth elevation rotation stages which allow the field of regard of the EBCs to move.
This paper proposes an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId.