Abstract
This paper presents an action recognition approach based on shape and action descriptors that is aimed at the classification of physical exercises under partial occlusion. Regular physical activity in adults can be seen as a form of non-communicable diseases prevention, and may be aided by digital solutions that encourages individuals to increase their activity level. The application scenario includes workouts in front of the camera, where either the lower or upper part of the camera’s field of view is occluded. The proposed approach uses various features extracted from sequences of binary silhouettes, namely centroid trajectory, shape descriptors based on the Minimum Bounding Rectangle, action representation based on the Fourier transform and leave-one-out cross-validation for classification. Several experiments combining various parameters and shape features are performed. Despite the presence of occlusion, it was possible to obtain about 90% accuracy for several action classes, with the use of elongation values observed over time and centroid trajectory.
Highlights
Means of Shape and ActionHuman action recognition is a very popular topic in a field of computer vision
This paper addresses the problem by applying shape generalization and a combination of simple shape features that are analysed over time
All silhouettes are represented using selected shape descriptor and the results are combined into a vector which is normalized
Summary
Human action recognition is a very popular topic in a field of computer vision. There is a large variety of applications associated with action recognition and classification based on Visual Content Analysis, some examples include surveillance systems [1,2], video retrieval and annotation [3], human–computer interaction [4,5], and quality-of-live improvement systems [6,7]. A focus is put on the recognition of actions that are classified based on exercise types. The WHO proposed some updated general recommendations on physical activity during pandemic, emphasizing several activity types that can be done at home, such as online exercise classes, dancing, playing active video games, jumping rope as well as muscle strength and balance training [20]. The proposed action recognition algorithm can be applied in a physical activity training system offering digital programs for home exercises. Using a taxonomy presented in [12], the proposed action representation belongs to the category of holistic representations that are based on global features of a human body shape and movements.
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