Learning Multi-target Tracking with Quadratic Object Interactions
We describe a model for multi-target tracking based
on associating collections of candidate detections across
frames of a video. In order to model pairwise interactions
between different tracks, such as suppression of overlapping
tracks and contextual cues about co-occurence of different
objects, we augment a standard min-cost flow objective with
quadratic terms between detection variables. We learn the
parameters of this model using structured prediction and a
loss function which approximates the multi-target tracking
accuracy. We evaluate two different approaches to finding
an optimal set of tracks under model objective based on an
LP relaxation and a novel greedy extension to dynamic pro-
gramming that handles pairwise interactions. We find the
greedy algorithm achieves equivalent performance to the
LP relaxation while being 2-7x faster than a commercial
solver. The resulting model with learned parameters out-
performs existing methods across several categories on the
KITTI tracking benchmark.
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Text Reference
Shaofei Wang and Charless C. Fowlkes. Learning multi-target tracking with quadratic object interactions. arXiv:, 2014. URL: http://arxiv.org/abs/1412.2066.BibTeX Reference
@article{WangF_TR_2014,author = "Wang, Shaofei and Fowlkes, Charless C.",
title = "Learning Multi-target Tracking with Quadratic Object Interactions",
journal = "arXiv:",
volume = "abs/1412.2066",
year = "2014",
url = "http://arxiv.org/abs/1412.2066",
tag = "object_recognition"
}