Multi-person dance tiered posture recognition with cross progressive multi-resolution representation integration
Huizhu Kao
TL;DR
This paper introduces a new method for accurately recognizing dance postures in multi-person settings by combining multi-resolution features and tiered recognition.
Contribution
The novel CPMRI and TPR modules enhance posture recognition by integrating multi-resolution features and addressing joint matching in complex dance scenarios.
Findings
The CPMRI module improves feature representation by combining high- and low-level features effectively.
The TPR module enhances key point accuracy by classifying and progressively refining torso and extremity joints.
Experiments on MSCOCO2017 and a Chinese dance dataset show superior performance in posture recognition metrics.
Abstract
Recognizing postures in multi-person dance scenarios presents challenges due to mutual body part obstruction and varying distortions across different dance actions. These challenges include differences in proximity and size, demanding precision in capturing fine details to convey action expressiveness. Robustness in recognition becomes crucial in complex real-world environments. To tackle these issues, our study introduces a novel approach, i.e., Multi-Person Dance Tiered Posture Recognition with Cross Progressive Multi-Resolution Representation Integration (CPMRI) and Tiered Posture Recognition (TPR) modules. The CPMRI module seamlessly merges high-level features, rich in semantic information, with low-level features that provide precise spatial details. Leveraging a cross progressive approach, it retains semantic understanding while enhancing spatial precision, bolstering the…
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Taxonomy
TopicsHuman Pose and Action Recognition · Hand Gesture Recognition Systems · Human Motion and Animation