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キーワード:
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要旨:
People tracking in crowded real-world scenes is challenging due to
frequent and long-term occlusions. Recent tracking methods obtain
the image evidence from object (people) detectors, but typically
use off-the-shelf detectors and treat them as black box components.
In this paper we argue that for best performance one should explicitly
train people detectors on failure cases of the overall tracker instead.
To that end, we first propose a novel joint people detector that
combines a state-of-the-art single person detector with a detector
for pairs of people, which explicitly exploits common patterns of
person-person occlusions across multiple viewpoints that are a common
failure case for tracking in crowded scenes. To explicitly address
remaining failure cases of the tracker we explore two methods. First,
we analyze typical failure cases of trackers and train a detector
explicitly on those failure cases. And second, we train the detector
with the people tracker in the loop, focusing on the most common
tracker failures. We show that our joint multi-person detector significantly
improves both detection accuracy as well as tracker performance,
improving the state-of-the-art on standard benchmarks.
