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Event-based approaches, which are based on bio-inspired asynchronous eventcameras, have achieved promising performance on various computer vision tasks.However, the study of the fundamental event data association problem is stillin its infancy. In this paper, we propose a novel Event Data Associationapproach (called EDA) to explicitly address the data association problem. Theproposed EDA seeks for event trajectories that best fit the event data, inorder to perform unifying data association. In EDA, we first asynchronouslygather the event data, based on its information entropy. Then, we introduce adeterministic model hypothesis generation strategy, which effectively generatesmodel hypotheses from the gathered events, to represent the corresponding eventtrajectories. After that, we present a two-stage weighting algorithm, whichrobustly weighs and selects true models from the generated model hypotheses,through multi-structural geometric model fitting. Meanwhile, we also propose anadaptive model selection strategy to automatically determine the number of thetrue models. Finally, we use the selected true models to associate the eventdata, without being affected by sensor noise and irrelevant structures. Weevaluate the performance of the proposed EDA on the object tracking task. Theexperimental results show the effectiveness of EDA under challenging scenarios,such as high speed, motion blur, and high dynamic range conditions.

Event Data Association via Robust Model Fitting for Event-based Object Tracking