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Track-On2: Enhancing Online Point Tracking with Memory.

Gorkay Aydemir, Weidi Xie, Fatma Guney

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 18, 2026
    PubMed
    Summary
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    Track-On2 is a new transformer model for online long-term point tracking. It achieves state-of-the-art results by using causal processing and a memory mechanism for robust tracking in real-time applications.

    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Long-term point tracking is challenging due to appearance changes, motion, and occlusion.
    • Online tracking requires frame-by-frame processing for real-time applications.

    Purpose of the Study:

    • To develop an efficient and effective online long-term point tracking model.
    • To improve upon existing tracking methods by addressing limitations in handling appearance changes and occlusions.

    Main Methods:

    • Introduced Track-On2, a transformer-based model extending the prior Track-On model.
    • Implemented causal frame processing with a memory mechanism to maintain temporal coherence.
    • Utilized coarse patch-level classification followed by refinement during inference.
    • Investigated synthetic training strategies to enhance temporal robustness.

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    Main Results:

    • Track-On2 achieved state-of-the-art performance on five synthetic and real-world benchmarks.
    • Outperformed prior online trackers and strong offline methods.
    • Demonstrated effectiveness in handling significant appearance changes, motion, and occlusion.

    Conclusions:

    • Causal, memory-based architectures trained on synthetic data are scalable solutions for real-world point tracking.
    • Track-On2 offers improved performance and efficiency for online long-term tracking applications.