Online Multi-Person Tracking by Tracker Hierarchy

Liliana Lo Presti, Liliana Lo Presti, Jianming Zhang, Stan Sclaroff

Risultato della ricerca: Otherpeer review

57 Citazioni (Scopus)

Abstract

Tracking-by-detection is a widely used paradigm for multi-person tracking but is affected by variations in crowd density, obstacles in the scene, varying illumination, human pose variation, scale changes, etc. We propose an improved tracking-by-detection framework for multi-person tracking where the appearance model is formulated as a template ensemble updated online given detections provided by a pedestrian detector. We employ a hierarchy of trackers to select the most effective tracking strategy and an algorithm to adapt the conditions for trackers’ initialization and termination. Our formulation is online and does not require calibration information. In experiments with four pedestrian tracking benchmark datasets, our formulation attains accuracy that is comparable to, or better than, the state-of-the-art pedestrian trackers that must exploit calibration information and operate offline.
Lingua originaleEnglish
Pagine379-385
Numero di pagine7
Stato di pubblicazionePublished - 2012

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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