Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes

TL;DR

This paper introduces a novel online multi-object tracking method for extremely crowded scenes by formulating the problem as a Binary Quadratic Program and solving it efficiently with an advanced optimization algorithm, enabling real-time tracking of hundreds of targets.

Contribution

It presents a new quadratic programming formulation for crowd tracking that integrates appearance, motion, and contextual cues, along with an efficient solver for high-density scenarios.

Findings

Successfully tracks hundreds of targets in crowded scenes.

Achieves significant improvements over state-of-the-art methods.

Operates efficiently in real-time conditions.

Abstract

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.