Comparative Analysis of Tail Tip and Hip Based Features for Dog Tail Wag Classification with Depth Cameras and Support Vector Machines
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i2.1252Keywords:
Tail Tip, Hip, Tail Wagging, Support Vector MachinesAbstract
Understanding dogs’ emotional patterns through their tail movement is key to strengthening the human-canine bond and improving their well-being. This study presents an innovative approach to identify the direction of tail movement (either left or right), using the dog’s hip as the primary reference point. Through a preprocessing and feature extraction process, a Support Vector Machine (SVM) classifier was trained with spatial data from the hip and compared to a classifier from a previous study. The results indicate that the classifier trained with hip data achieved a 99% score in accuracy, precision, and F1-score metrics. Additionally, the Friedman test was performed to verify whether there are statistically significant differences between the two classifiers.
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