AI ENHANCED VIDEO SEQUENCE ANALYSIS BY WAVELET NEURAL NETWORK WITH RANDOM FOREST

Video-based dance movement recognition technology plays a crucial role in various intelligent applications and finds extensive use in theater industries, particularly in training intelligent dance assistants. This method enables the reconstruction of dancers' postures by extracting features from their images, thereby facilitating the examination and correction of postures to recognize their dance movements accurately. Effective feature extraction is pivotal in this technology, with deep learning emerging as one of the most effective approaches for extracting features from video data. Recognizing specific dance movements is essential for understanding dancer actions and behaviors. However, getting high recognition accuracy is still hard, especially when there aren't many samples and when it's hard to catch the different types of dance activities that happen in space and in the world. To address these challenges and enhance the design of intelligent choreography design and rehearsal optimization, our focus is on machine learning-based dance action recognition model. We use the InceptionV3 pretrained CNN architecture to extract features from the spatial-temporal level of the video data. The modified cuckoo optimisation (MCO) algorithm is then used to choose the best features from a group of options. We also use the wavelet neural network with random forest (WNN-RF) machine learning model to correctly identify dance moves in the video clips. We run tests on a large dance video dataset with 13,400 videos to see how well our proposed model works and how it compares to other methods. This research aims to enhance the efficiency and refinement of choreography design production and provide optimal rehearsal solutions for theater dancers.