  Event-based approaches, which are based on bio-inspired asynchronous event
cameras, have achieved promising performance on various computer vision tasks.
However, the study of the fundamental event data association problem is still
in its infancy. In this paper, we propose a novel Event Data Association
(called EDA) approach to explicitly address the event association and fusion
problem. The proposed EDA seeks for event trajectories that best fit the event
data, in order to perform unifying data association and information fusion. In
EDA, we first asynchronously fuse the event data based on its information
entropy. Then, we introduce a deterministic model hypothesis generation
strategy, which effectively generates model hypotheses from the fused events,
to represent the corresponding event trajectories. After that, we present a
two-stage weighting algorithm, which robustly weighs and selects true models
from the generated model hypotheses, through multi-structural geometric model
fitting. Meanwhile, we also propose an adaptive model selection strategy to
automatically determine the number of the true models. Finally, we use the
selected true models to associate and fuse the event data, without being
affected by sensor noise and irrelevant structures. We evaluate the performance
of the proposed EDA on the object tracking task. The experimental results show
the effectiveness of EDA under challenging scenarios, such as high speed,
motion blur, and high dynamic range conditions.
