# Multi-person dance tiered posture recognition with cross progressive multi-resolution representation integration

## 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.

## Key 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 network’s feature representation capabilities. Through innovative feature concatenation techniques, it efficiently blends high-resolution and low-resolution features, forming a comprehensive multi-resolution representation. This approach significantly improves posture recognition robustness, especially in intricate dance postures involving scale variations. The TPR module classifies body key points into core torso joints and extremity joints based on distinct distortion characteristics. Employing a three-tier tiered network, it progressively refines posture recognition. By addressing the optimal matching problem between torso and extremity joints, the module ensures accurate connections, refining the precision of body key point locations. Experimental evaluations against state-of-the-art methods using MSCOCO2017 and a custom Chinese dance dataset validate our approach’s effectiveness. Evaluation metrics including Object Keypoint Similarity (OKS)-based Average Precision (AP), mean Average Precision (mAP), and Average Recall (AR) underscore the efficacy of the proposed method.