Classifying Global Scene Context for On-line Multiple Tracker Selection - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

Classifying Global Scene Context for On-line Multiple Tracker Selection

Résumé

In this paper, we present a novel framework for combining several independent on-line trackers using visual scene context. The aim of our method is to decide automatically at each point in time which specific tracking algorithm works best under the given scene or acquisition conditions. To this end, we define a set of generic global context features computed on each frame of a set of training videos. At the same time, we record the performance of each individual tracker on these videos in terms of object bounding box overlap with the ground truth. Then a classifier is trained to estimate which tracker gives the best result given the global scene context in a particular frame. We experimentally show that such a classifier can predict the best tracker with a precision of over 80% in unknown videos with unknown environments. The proposed tracking method further filters the classifier responses temporarily using a Hidden Markov Model in order to avoid rapid oscillations between different trackers. Finally, we evaluated the overall tracking system and showed that this scene context-based tracker selection considerably improves the overall robustness and compares favourably with the state-of-the-art.
Fichier principal
Vignette du fichier
bmvc_CRC(official).pdf (562.93 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01208200 , version 1 (02-10-2015)

Identifiants

  • HAL Id : hal-01208200 , version 1

Citer

Salma Moujtahid, Stefan Duffner, Atilla Baskurt. Classifying Global Scene Context for On-line Multiple Tracker Selection. British Machine Vision Conference (BMVC), Sep 2015, Swansea, United Kingdom. ⟨hal-01208200⟩
227 Consultations
260 Téléchargements

Partager

Gmail Facebook X LinkedIn More