Abstract
OAPA A depth estimation algorithm based on vergence vision using a mechanical joint attached to two cameras is proposed. A Gaussian pyramid template-matching approach is used to align the view of the slave camera to the fixation point of the master camera. The master camera uses an object detection algorithm to find the target’s centroid and centers it relative to the image coordinates. Then, vergence movement of the slave camera is performed using a pyramid normalized cross-correlation algorithm. Simple geometric triangulation is employed to compute the depth of that target. This proposed method was implemented using an active binocular vision platform with five degrees of freedom where four degrees of freedom to control the pan and tilt independently, and one degree of freedom to control the baseline which is the distance between the camera. This system was designed for implementation in agriculture harvesting applications. Analysis of field trial results indicates a worst-case precision of a target tomatoes’ depth to be ±1.32 cm at a depth of 85 cm.
DOI
10.1109/ACCESS.2018.2877721
Publication Date
2018-10-23
Publication Title
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
ISSN
2169-3536
Embargo Period
2024-11-22
Recommended Citation
Mohamed, A., Culverhouse, P., Cangelosi, A., & Yang, C. (2018) 'Depth Estimation Based on Pyramid Normalized Cross-correlation Algorithm for Vergence Control', IEEE Access, . Institute of Electrical and Electronics Engineers (IEEE): Available at: https://doi.org/10.1109/ACCESS.2018.2877721
