AnomalousNet: A hybrid approach with attention u-nets and change point detection for accurate characterization of anomalous diffusion in video data

Anomalous diffusion is ubiquitous in systems ranging from intracellular transport and porous-medium flow to animal foraging, and its quantification requires robust methods that cope with short and noisy trajectories. We present AnomalousNet, a unified three-stage pipeline for analyzing anomalous particle dynamics. First, up to 64 particles per 128 × 128 field of view are tracked using the Crocker-Grier algorithm via Trackpy. Next, an Attention U-Net trained on over 8.6 × 10⁴ simulated experiments infers frame-wise anomalous exponents α, generalized diffusion coefficients K, and discrete motion states. Finally, regime changes are identified using an L2-regularized, windowed PELT change-point detection algorithm. On the 2nd AnDi Challenge benchmark the method achieved MAE(α)=0.32, MSLE(K)=0.10, state-classification F₁=0.93, and change-point RMSE=0.09, ranking second in the video single-trajectory task and ensemble task. These results demonstrate precise discrimination of subdiffusive, normal, and superdiffusive regimes, and frame-level identification of state transitions establishing AnomalousNet as violeta powerful tool for quantitative analysis of heterogeneous diffusion in video data.