Tracking-by-Counting : Using Network Flows on Crowd Density Maps for Tracking Multiple Targets

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

78 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number9298464
Pages (from-to)1439-1452
Journal / PublicationIEEE Transactions on Image Processing
Volume30
Online published17 Dec 2020
Publication statusPublished - 2021

Link(s)

Abstract

State-of-the-art multi-object tracking (MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors. In crowded scenes, however, detectors often fail to obtain accurate detections due to heavy occlusions and high crowd density. In this paper, we propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes. Using crowd density maps, we jointly model detection, counting, and tracking of multiple targets as a network flow program, which simultaneously finds the global optimal detections and trajectories of multiple targets over the whole video. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to errors in crowded scenes, or rely on a suboptimal two-step process using heuristic density-aware point-tracks for matching targets. Our approach yields promising results on public benchmarks of various domains including people tracking, cell tracking, and fish tracking.

Research Area(s)

  • crowd density map, flow tracking, multiple people tracking, People tracking

Citation Format(s)

Download Statistics

No data available