Abstract
Extracting and recognizing complex human movements from unconstraint online video sequence is an interesting task. In this paper the complicated problem from the class is approached using unconstraint video sequences belonging to Indian classical dance forms. A new segmentation model is developed using discrete wavelet transform and local binary pattern (LBP) features for segmentation. A 2D point cloud is created from the local human shape changes in subsequent video frames. The classifier is fed with 5 types of features calculated from Zernike moments, Hu moments, shape signature, LBP features, and Haar features. We also explore multiple feature fusion models with early fusion during segmentation stage and late fusion after segmentation for improving the classification process. The extracted features input the Adaboost multiclass classifier with labels from the corresponding song (tala). We test the classifier on online dance videos and on an Indian classical dance dataset prepared in our lab. The algorithms were tested for accuracy and correctness in identifying the dance postures.
Highlights
Automatic human action recognition is a complicated problem for computer vision scientists, which involves mining and categorizing spatial patterns of human poses in videos
For removing video frame noise during capture and to extract local shape information, we propose a hybrid algorithm with Discrete wavelet transform (DWT) [34] and Local Binary Patterns (LBP) [35]
Hand and leg shape segmentation is a critical part of a Indian Classical Dance (ICD)
Summary
Automatic human action recognition is a complicated problem for computer vision scientists, which involves mining and categorizing spatial patterns of human poses in videos. Human action is defined as a temporal variation of human body in a video sequence, which can be any action such as dancing, running, jumping, or walking. Automation encompasses mining the video sequences with computer algorithms for identifying similarities between actions in the unknown query dataset with that of the known dataset. Last decade has seen a jump in online video creation and the need for algorithms that can search within the video sequence for a specific human pose or object of interest. The problem is to extract, identify a human pose, and classify into labels based on trained human signature action models [1]. The objective of this work is to extract the signature of Indian classical dance poses from both online and offline videos given a specific dance pose sequence as input
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