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