In this paper we present a novel strategy for modelling the motion of local patches for single object tracking that can be seamlessly applied to most part-based trackers in the literature. The proposed Adaptive Local Movement Modelling (ALMM) method is able to model the local spatial distribution of the image patches defining the object to track and the reliability of each image patch. Given the output of a base tracking algorithm, a Gaussian Mixture Model (GMM) is first used to model the distribution of the movement of local patches relative to the gravity center of the tracked object. Then, the GMM is combined with the base tracker in a boosting framework, which gives a novel integrated boosting classifier for the tracking task. This provides a robust procedure to detect outliers in the local motion of the patches. The algorithm is highly configurable with the possibility to change the number of local patches used for tracking and to adapt to the variations of the tracked object. Tracking results on standard datasets show that equipping state-of-the-art trackers with our tehcnique remarkably improves their performance

Adaptive local movement modelling for object tracking

PERINA, Alessandro;MURINO, Vittorio
2015

Abstract

In this paper we present a novel strategy for modelling the motion of local patches for single object tracking that can be seamlessly applied to most part-based trackers in the literature. The proposed Adaptive Local Movement Modelling (ALMM) method is able to model the local spatial distribution of the image patches defining the object to track and the reliability of each image patch. Given the output of a base tracking algorithm, a Gaussian Mixture Model (GMM) is first used to model the distribution of the movement of local patches relative to the gravity center of the tracked object. Then, the GMM is combined with the base tracker in a boosting framework, which gives a novel integrated boosting classifier for the tracking task. This provides a robust procedure to detect outliers in the local motion of the patches. The algorithm is highly configurable with the possibility to change the number of local patches used for tracking and to adapt to the variations of the tracked object. Tracking results on standard datasets show that equipping state-of-the-art trackers with our tehcnique remarkably improves their performance
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/961721
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? ND
social impact