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Optimizing Image Target Tracking
To ensure the highest quality image target tracking experience, be sure to follow these guidelines when selecting an image target.
Before training the Image Targets, you must prepare the images in digital format. The available formats are
- .jpg/jpeg
- .png
- .bmp
A good Image Target will have certain visual features that enables accurate pose estimation and stable tracking :
Properties | Explanation |
Distinctive pattern | A texture with evenly distributed and distinctive visual features (e.g. corners, blobs) |
No repetitive pattern | Textures with no similar patterns repeating in space |
High contrast | Image with high contrast |
Non-reflective | Print on a matte surface that will not reflect light when printed |
Image Targets that possess the following attributes will enable the best detection and tracking performance from the the SQUARS viewer. A basic rule is that complex and detailed images on well lit flat surfaces work best.
Examples of good target images:
.png?alt=media&token=52dd874e-7c0a-4001-8a15-b58940c1f53e)
Examples of bad target images:
.png?alt=media&token=cf3aa9e4-2e45-4864-ba4a-ff402ab0e0df)
Image Targets are detected by analyzing the inherent features extracted from the target image and comparing them in real-time with the features in the live camera image. Targets are given a rating of either 'good', 'normal', or 'bad'. Although targets with lower ratings ('bad' or 'normal') can still be detected and tracked, it is advisable to use a 'good' rating target.
To create a target that is accurately detected, you should use images according to the above attributes for an ideal Image Target.
Last modified 4mo ago