CVPR 2023, award candidate – Data-driven Feature Tracking for Event Cameras

In this episode we discuss Data-driven Feature Tracking for Event Cameras
by Nico Messikommer, Carter Fang, Mathias Gehrig, Davide Scaramuzza. The paper details a data-driven feature tracking method for event cameras that improves upon existing techniques that require parameter tuning and struggle with noise and generalization. The proposed method utilizes a frame attention module to share information across feature tracks, resulting in improved performance with a 120% increase in relative feature age and lower latency compared to existing approaches. Multimedia materials and code are available to supplement the paper.