Robust observations for object tracking

TitleRobust observations for object tracking
Publication TypeConference Papers
Year of Publication2005
AuthorsHan B, Davis LS
Conference NameImage Processing, 2005. ICIP 2005. IEEE International Conference on
Date Published2005/09//
Keywords(numerical, adaptive, analysis;, component, enhancement;, filter, Filtering, framework;, image, images;, likelihood, methods);, object, observation, particle, PCA;, principal, tracking;

It is a difficult task to find an observation model that will perform well for long-term visual tracking. In this paper, we propose an adaptive observation enhancement technique based on likelihood images, which are derived from multiple visual features. The most discriminative likelihood image is extracted by principal component analysis (PCA) and incrementally updated frame by frame to reduce temporal tracking error. In the particle filter framework, the feasibility of each sample is computed using this most discriminative likelihood image before the observation process. Integral image is employed for efficient computation of the feasibility of each sample. We illustrate how our enhancement technique contributes to more robust observations through demonstrations.