.png){kind=icon}
.png){kind=icon}
Return
Automatic gait analysis in canines using computer vision
.png){kind=icon}
OA
AI
DOI：10.3389/fvets.2026.1729697.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Automated canine gait analysis using computer vision has the potential to extend objective canine gait assessment beyond specialized; controlled laboratories into domestic environments; but the field is comparatively less mature than human methods. This review explores the state-of-the-art for vision-based canine gait analysis; with a particular emphasis on single-camera (monocular) articulated pose and shape reconstruction; along with the extraction and interpretation of clinically relevant gait parameters. Across the literature; current pipelines reconstruct anatomical and surface representations of canines from images and video; yet rarely achieve the biomechanical fidelity or validation against gold-standard references such as motion capture; pressure walkways or fluoroscopy. Three requirements emerge from the literature: robust monocular 3D reconstruction sufficiently accurate to measure soft-tissue artifacts (approximately 10–20 mm); a standardized set of gait parameters aligned to veterinary assessment practices; and a shift from parts-based to holistic gait analysis. We highlight priority research directions to improve monitoring of canine gait in real-world settings including breed-representative datasets; synthetic training data to real-world data adaption; and ensemble learning for pathology identification. Addressing these gaps could allow for objective; longitudinal monitoring of canine gait in both veterinary practices and domestic environments. We advocate for increased interdisciplinary collaboration to foster innovation and establish new standards in the field.
Keywords:
computer vision
locomotion
pose estimation
canine gait analysis
mesh reconstruction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.9
Papers:
3.2K
Citations:
2.6W