Adrien Gaidon, Eleonora Vig
26th British Machine Vision Conference, Swansea, UK, 7-10 September, 2015.
Automatically detecting, labeling, and tracking objects in videos depends first and
foremost on accurate category-level object detectors. These might, however, not always
be available in practice, as acquiring high-quality large scale labeled training datasets is
either too costly or impractical for all possible real-world application scenarios. A scalable
solution consists in re-using object detectors pre-trained on generic datasets. This
work is the first to investigate the problem of on-line domain adaptation of object detectors
for causal multi-object tracking (MOT). We propose to alleviate the dataset bias by
adapting detectors from category to instances, and back: (i) we jointly learn all target
models by adapting them from the pre-trained one, and (ii) we also adapt the pre-trained
model on-line. We introduce an on-line multi-task learning algorithm to efficiently share
parameters and reduce drift, while gradually improving recall. Our approach is applicable
to any linear object detector, and we evaluate both cheap “mini-Fisher Vectors” and
expensive “off-the-shelf” ConvNet features. We quantitatively measure the benefit of
our domain adaptation strategy on the KITTI tracking benchmark and on a new dataset
(PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.
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