Frame-Event Alignment and Fusion Network for High Frame Rate Tracking
Jiqing Zhang, Yuanchen Wang, Wenxi Liu, Meng Li, Jinpeng Bai, Baocai, Yin, Xin Yang

TL;DR
This paper introduces a novel end-to-end network that combines conventional RGB frames and event-based camera data to enable high frame rate object tracking, significantly surpassing existing methods in challenging conditions.
Contribution
It proposes a multi-modality alignment and fusion network that effectively integrates frame and event data for high frame rate tracking, a novel approach in this domain.
Findings
Outperforms state-of-the-art trackers in high frame rate scenarios
Achieves tracking up to 240Hz on the FE240hz dataset
Effectively handles challenging conditions with multi-modality fusion
Abstract
Most existing RGB-based trackers target low frame rate benchmarks of around 30 frames per second. This setting restricts the tracker's functionality in the real world, especially for fast motion. Event-based cameras as bioinspired sensors provide considerable potential for high frame rate tracking due to their high temporal resolution. However, event-based cameras cannot offer fine-grained texture information like conventional cameras. This unique complementarity motivates us to combine conventional frames and events for high frame rate object tracking under various challenging conditions. Inthispaper, we propose an end-to-end network consisting of multi-modality alignment and fusion modules to effectively combine meaningful information from both modalities at different measurement rates. The alignment module is responsible for cross-style and cross-frame-rate alignment between frame…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Air Quality Monitoring and Forecasting · Advanced Chemical Sensor Technologies
