Tracking Objects with Higher Order Interactions via Delayed Column Generation
Shaofei Wang, Steffen Wolf, Charless Fowlkes, Julian Yarkony
Abstract
We study the problem of multi-target tracking and data association in video. We formulate this in terms of selecting a subset of high-quality tracks subject to the constraint that no pair of selected tracks is associated with a common detection (of an object). This objective is equivalent to the classic NP-hard problem of finding a maximum-weight set packing (MWSP) where tracks correspond to sets and is made further difficult since the number of candidate tracks grows exponentially in the number of detections. We present a relaxation of this combinatorial problem that uses a column generation formulation where the pricing problem is solved via dynamic programming to efficiently explore the space of tracks. We employ row generation to tighten the bound in such a way as to preserve efficient inference in the pricing problem. We show the practical utility of this algorithm for pedestrian and particle tracking.
BibTeX
@InProceedings{pmlr-v54-wang17c,
title = {{Tracking Objects with Higher Order Interactions via Delayed Column Generation}},
author = {Wang, Shaofei and Wolf, Steffen and Fowlkes, Charless and Yarkony, Julian},
booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
pages = {1132--1140},
year = {2017},
editor = {Singh, Aarti and Zhu, Jerry},
volume = {54},
series = {Proceedings of Machine Learning Research},
month = {20--22 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v54/wang17c/wang17c.pdf},
url = {https://proceedings.mlr.press/v54/wang17c.html},
abstract = {We study the problem of multi-target tracking and data association in video. We formulate this in terms of selecting a subset of high-quality tracks subject to the constraint that no pair of selected tracks is associated with a common detection (of an object). This objective is equivalent to the classic NP-hard problem of finding a maximum-weight set packing (MWSP) where tracks correspond to sets and is made further difficult since the number of candidate tracks grows exponentially in the number of detections. We present a relaxation of this combinatorial problem that uses a column generation formulation where the pricing problem is solved via dynamic programming to efficiently explore the space of tracks. We employ row generation to tighten the bound in such a way as to preserve efficient inference in the pricing problem. We show the practical utility of this algorithm for pedestrian and particle tracking.}
}