  Multi-object tracking has been studied for decades. However, when it comes to
tracking pedestrians in extremely crowded scenes, we are limited to only few
works. This is an important problem which gives rise to several challenges.
Pre-trained object detectors fail to localize targets in crowded sequences.
This consequently limits the use of data-association based multi-target
tracking methods which rely on the outcome of an object detector. Additionally,
the small apparent target size makes it challenging to extract features to
discriminate targets from their surroundings. Finally, the large number of
targets greatly increases computational complexity which in turn makes it hard
to extend existing multi-target tracking approaches to high-density crowd
scenarios. In this paper, we propose a tracker that addresses the
aforementioned problems and is capable of tracking hundreds of people
efficiently. We formulate online crowd tracking as Binary Quadratic Programing.
Our formulation employs target's individual information in the form of
appearance and motion as well as contextual cues in the form of neighborhood
motion, spatial proximity and grouping constraints, and solves detection and
data association simultaneously. In order to solve the proposed quadratic
optimization efficiently, where state-of art commercial quadratic programing
solvers fail to find the answer in a reasonable amount of time, we propose to
use the most recent version of the Modified Frank Wolfe algorithm, which takes
advantage of SWAP-steps to speed up the optimization. We show that the proposed
formulation can track hundreds of targets efficiently and improves state-of-art
results by significant margins on eleven challenging high density crowd
sequences.
