title: Adaptive object tracking based on an effective appearance filter.
abstract: We propose a similarity measure based on a Spatial-color Mixture of Gaussians (SMOG) appearance model for particle filters. This improves on the popular similarity measure based on color histograms because it considers not only the colors in a region but also the spatial layout of the colors. Hence, the SMOG-based similarity measure is more discriminative. To efficiently compute the parameters for SMOG, we propose a new technique, with which the computational time is greatly reduced. We also extend our method by integrating multiple cues to increase the reliability and robustness. Experiments show that our method can successfully track objects in many difficult situations.
authors.affiliation: Department of Computer Science, John Hopkins University, Baltimore, MD 21218, USA. hwang@cs.jhu.edu
authors.forename: Hanzi
authors.initials: H
authors.lastname: Wang
authors.forename: David
authors.initials: D
authors.lastname: Suter
authors.forename: Konrad
authors.initials: K
authors.lastname: Schindler
authors.forename: Chunhua
authors.initials: C
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D009038:Motion; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity