Smart Steps : Enhancing Dance Through AI Driven Analysis

The field of dance training and evaluation is vital for dancers to enhance their skills and artistic expression. However, current evaluation methods often rely on subjective assessments, leading to inconsistent and biased evaluations, thereby hindering dancers' ability to assess progress and identify areas for improvement. Furthermore, the lack of objective evaluation methods poses challenges in setting realistic goals and monitoring progress over time. In response to these challenges, this paper proposes an automated system designed to provide accurate and objective evaluations of dancers' performances.Our proposed system utilizes advanced techniques in computer vision and pose extraction to analyse videos of both a dance trainer and a dancer attempting to replicate the trainer’s moves. Key body landmarks are extracted from both videos to represent poses, enabling a detailed comparison between them. The system employs a method of comparing poses by analysing the direction of each limb and computing a percentage match between the poses. The results provide dancers with quantitative feedback on their performance, allowing them to accurately track progress and identify areas for improvement. By providing objective evaluations, our system ensures fair assessments across different dancers, fostering continuous improvement in their skills and artistic expression.