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Abstract:
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We propose a data-driven approach to online
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multi-object tracking (MOT) that uses a convolutional neural
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network (CNN) for data association in a tracking-by-detection
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framework. The problem of multi-target tracking aims to assign
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noisy detections to a-priori unknown and time-varying number
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of tracked objects across a sequence of frames. A majority
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of the existing solutions focus on either tediously designing
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cost functions or formulating the task of data association as
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a complex optimization problem that can be solved effectively.
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Instead, we exploit the power of deep learning to formulate the
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data association problem as inference in a CNN. To this end, we
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propose to learn a similarity function that combines cues from
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both image and spatial features of objects. Our solution learns
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to perform global assignments in 3D purely from data, handles
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noisy detections and a varying number of targets, and is easy
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to train. We evaluate our approach on the challenging KITTI
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dataset and show competitive results. Our code is available at
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https://git.uwaterloo.ca/wise-lab/fantrack. |
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