Fast planar object detection and tracking via edgel templates

Taehee Lee, Stefano Soatto

Abstract: We describe an efficient method to detect and track planar objects using a template of edge segments. Such segments are selected at multiple scales based on gradient magnitude; their positions and orientations are used to determine a canonical reference frame where the descriptor is computed based on quantized orientation. The resulting descriptors are efficiently matched using logical operations, and tracked between frames. The method yields pose estimates that are robust to scale changes, foreshortening, partial occlusions, and is suitable for use in augmented reality and human-computer interaction.