Automatic Parameter Adaptation for Multi-object Tracking - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

Automatic Parameter Adaptation for Multi-object Tracking


Object tracking quality usually depends on video context (e.g. object occlusion level, object density). In order to decrease this dependency, this paper presents a learning approach to adapt the tracker parameters to the context variations. In an offline phase, satisfactory tracking parameters are learned for video context clusters. In the online control phase, once a context change is detected, the tracking parameters are tuned using the learned values. The experimental results show that the proposed approach outperforms the recent trackers in state of the art. This paper brings two contributions: (1) a classification method of video sequences to learn offline tracking parameters, (2) a new method to tune online tracking parameters using tracking context.
Fichier principal
Vignette du fichier
paper_ICVS13.pdf (1.52 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00821669 , version 1 (11-05-2013)



Duc Phu Chau, Monique Thonnat, François Bremond. Automatic Parameter Adaptation for Multi-object Tracking. International Conference on Computer Vision Systems (ICVS), Jul 2013, St Petersburg, Russia. ⟨hal-00821669⟩
162 View
333 Download



Gmail Facebook X LinkedIn More