Comparative Study of Adaptive Segmentation Techniques for Gesture Analysis in Unconstrained Environments
Notice bibliographique
Résumé
This paper discusses the importance of providing non- invasive techniques in the analysis of the complex movements performed by musicians and athletes. Current gesture analysis systems are insufficient and do not succeed in providing quality performance measurements without imposing strict environmental and operational constraints on individuals. Computer vision offers the means with which such techniques can be made possible without impeding the performance of musicians or compromising the integrity of the measurements. The following study compares some of the modern image segmentation techniques and discusses their shortcomings with respect to the stated context. A novel statistical method is also introduced in an attempt to improve the resilience of vision-based gesture segmentation. I. INTRODUCTION With the advent of more powerful computing facilities and advances in artificial intelligence, few techniques have been proposed in the evaluation of the complex movements inherent in the performances of musicians and athletes. Such techniques are highly desirable in order to detect problematic situations which often lead to chronic injuries and to provide the capacity with which professors and trainers may measure the evolution and habits of individuals. Gesture analysis provides the means with which quantitative measurements may be obtained with respect to the physical performances of musicians and athletes. Modern gesture analysis methodologies employed by professionals fail to obtain an exhaustive evaluation or a complete comparison between gestures. Current techniques in this type of analysis are not robust enough to operate in the unconstrained environments in which these performances must be evaluated. Many techniques today still rely on the use of encumbering sensor technologies and require the use of cabling or attaching markers on an individual. The techniques also often require a very controlled environment and involve tedious, expensive and complex operating methodologies. The imposed environments are usually foreign to the musicians or athletes resulting in an impeded performance and corruption to the exactitude of the measurements. The undertaken study aims to research and develop new vision-based methodologies suitable for the analysis of musician gestures. Specifically the context being explored consists of analyzing the gestures and postures of pianists with applications to piano pedagogy. Computer vision techniques offer the ideal means for this type of evaluation. Due to their non-invasive nature they do not interfere with the performance of pianists. Computer vision techniques are put to the test in a context where the imposed constraints on the environment and individuals must remain minimal.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».