Multi-object shape estimation and tracking from silhouette cues

TLDR: This paper proposes a new algorithm to automatically detect and reconstruct scenes with a variable number of dynamic objects, and distinguishes between m different shapes in the scene by using automatically learnt view-specific appearance models, eliminating the color calibration requirement.

Abstract: This paper deals with the 3D shape estimation from silhouette cues of multiple moving objects in general indoor or outdoor 3D scenes with potential static obstacles, using multiple calibrated video streams. Most shape-from-silhouette techniques use a two-classification of space occupancy and silhouettes, based on image regions that match or disagree with a static background appearance model. Binary silhouette information becomes insufficient to unambiguously carve 3D space regions as the number and density of dynamic objects increases. In such difficult scenes, multi-view stereo methods suffer from visibility problems, and rely on color calibration procedures tedious to achieve outdoors. We propose a new algorithm to automatically detect and reconstruct scenes with a variable number of dynamic objects. Our formulation distinguishes between m different shapes in the scene by using automatically learnt view-specific appearance models, eliminating the color calibration requirement. Bayesian reasoning is then applied to solve the m-shape occupancy problem, with m updated as objects enter or leave the scene. Results show that this method yields multiple silhouette-based estimates that drastically improve scene reconstructions over traditional two-label silhouette scene analysis. This enables the method to also efficiently deal with multi-person tracking problems.