In this paper, we propose 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 study proposes an event filter-based phase correlation template match (EF-PCTM) method, including its optimal design procedure, to measure micro-vibrations using an event camera. In this study, event filter is designed using an infinite impulse response filter and genetic algorithm, and a cost function is defined to improve the performance of EF-PCTM.
Faces in Event Streams dataset contains 689 minutes of recorded event streams, and 1.6 million annotated faces with bounding box and five point facial landmarks. This paper presents the dataset and corresponding models for detecting face and facial landmakrs directly from event stream data.
In this paper, we present an asynchronous linear filter architecture, fusing event and frame camera data, for HDR video reconstruction and spatial convolution that exploits the advantages of both sensor modalities. The key idea is the introduction of a state that directly encodes the integrated or convolved image information and that is updated asynchronously as each event or each frame arrives from the camera.
We show that our proposed pipeline provides improved accuracy over the result of the state-of-the-art visual odometry for stereo event-based cameras, while running in real-time on a standard CPU (low-resolution cameras). To the best of our knowledge, this is the first published visual-inertial odometry for stereo event-based cameras.