Adaptive Local Movement Modeling for Robust Object Tracking

• Published in: IEEE transactions on circuits and systems for video technology 2017-07, Vol.27 (7), p.1515-1526
• Main Authors: Baochang Zhang, Zhigang Li, Perina, Alessandro, Del Bue, Alessio, Murino, Vittorio, Jianzhuang Liu
• Format: Article
• Language: English
• Subjects: Adaptation models; Boosting; Center of gravity; Data analysis; Gaussian distribution; Gaussian mixture model (GMM); Gravity; Modelling; Movement; Object tracking; online learning; Outliers (statistics); Patches (structures); Reliability; Robustness; Standard data; Target tracking; Tracking
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2016.2540978

In this paper, we present a new strategy for modeling 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 modeling method is able to model the local movement 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 center of gravity of the tracked object. Then, the GMM is combined with the chosen base tracker in a boosting framework, which gives an efficient integrated scheme 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. The extensive tracking results on standard data sets show that equipping state-of-the-art trackers with our technique remarkably improves their performance.