FreqTrack: Frequency Learning based Vision Transformer for RGB-Event Object Tracking

• URL: http://arxiv.org/abs/2604.14526v1
• Date: Thu, 16 Apr 2026 01:47:22 GMT
• Title: FreqTrack: Frequency Learning based Vision Transformer for RGB-Event Object Tracking
• Abstract summary: FreqTrack is a frequency-aware RGBE tracking framework that establishes complementary inter-modal correlations through frequency-domain transformations.<n>FreqTrack achieves leading precision of 76.6% on the COESOT benchmark, validating the effectiveness of frequency-domain modeling for RGBE tracking.
• Score: 9.837735583393266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing single-modal RGB trackers often face performance bottlenecks in complex dynamic scenes, while the introduction of event sensors offers new potential for enhancing tracking capabilities. However, most current RGB-event fusion methods, primarily designed in the spatial domain using convolutional, Transformer, or Mamba architectures, fail to fully exploit the unique temporal response and high-frequency characteristics of event data. To address this, we1 propose FreqTrack, a frequency-aware RGBE tracking framework that establishes complementary inter-modal correlations through frequency-domain transformations for more robust feature fusion. We design a Spectral Enhancement Transformer (SET) layer that incorporates multi-head dynamic Fourier filtering to adaptively enhance and select frequency-domain features. Additionally, we develop a Wavelet Edge Refinement (WER) module, which leverages learnable wavelet transforms to explicitly extract multi-scale edge structures from event data, effectively improving modeling capability in high-speed and low-light scenarios. Extensive experiments on the COESOT and FE108 datasets demonstrate that FreqTrack achieves highly competitive performance, particularly attaining leading precision of 76.6\% on the COESOT benchmark, validating the effectiveness of frequency-domain modeling for RGBE tracking.

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