Bounding Box Dynamics in Visual Tracking: Modeling and Noise Covariance Estimation

Abstract

Common visual tracking algorithms make use of bounding box (BB) motion models. These models are parameterized by quantities such as noise covariance parameters corresponding to the evolution of the position, velocity, aspect ratio, width, or height of the bounding box, or their respective velocities. The noise covariance parameters are often hand-selected, using no principled knowledge regarding various aspects such as camera pose or frame rate. This paper aims to analyze how these aspects influence parameter estimates obtained from annotated datasets that are well-known to the visual tracking community. To obtain the estimates, the recently developed measurement difference method (MDM) is modified and used.