Convex-based lightweight feature descriptor for Augmented Reality Tracking
Indhumathi S., Christopher Clement J.
TL;DR
This paper introduces a new feature descriptor for augmented reality that is faster and more accurate than existing methods.
Contribution
The novel Convex Based Feature Descriptor (CBFD) system is introduced for efficient and robust augmented reality tracking.
Findings
CBFD achieves an average precision of 0.97, outperforming existing feature descriptors like Superpoint and SIFT.
CBFD has a computation time of 2.8 ms, which is 6.7% faster than other algorithms.
CBFD shows minimal feature location distance compared to DITF and HOG.
Abstract
Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. We have developed two filters capable of computing pixel intensity variations, followed by the covariance matrix of the polynomial to describe the features. The superiority of CBFD is validated through precision, recall, computation time, and feature location distance. Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Vision and Imaging · Video Surveillance and Tracking Methods