# Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors
###### 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. shape analysis; foreground occlusion; action recognition; exercise classification; silhouette sequences +
Footnote †: journal:  
related to the COVID-19 pandemic has been forcing people to change their daily routines and lockdowns, in particular, have been limiting physical activity [14; 15]. There are some studies from all over the world researching the adverse consequences of the pandemic on different aspects of life and well-being that indicate the importance of physical activities for health improvement [16; 17; 18; 19]. 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]. A narrative review in [15] summarises physical activity guidelines from 29 papers and gives some final joint recommendations. Generally, exercise types should include aerobic activity (e.g., running, walking), muscle strength activity (e.g., squats, various jumps), flexibility-stretching and relaxation (e.g., stretching arms, bending, yoga), and balance training. A brief report presented in [14] provides the results of a survey on the preferred home exercises for digital programs. Nearly 70% out of 15,261 participants from different countries were willing to work out at home, and were mostly interested in flexibility, resistance (strength training) and endurance (e.g., aerobic training) exercises. In our works, a specific application scenario is assumed in which a person performs exercises in front of a computer camera, based on the displayed examples. Camera captures exercises and the system analyses them during recognition process. This can be related to active video games and exercise classes online. In the examined scenario, a focus is put on incomplete action data, where a part of a silhouette is occluded as a result of the improper camera positioning or the person being at the wrong distance from the camera. Then, usually the upper or lower part of the video frame is occluded and a part of foreground silhouette is missing (above the shoulders or below the knees). Due to the fact that each pose can be affected in different manner, an action descriptor should relay on the pose variability (changes between frames) rather than an exact shape contour itself. Effective recognition needs robust shape features which can be calculated despite the lack of a part of a silhouette. This paper addresses the problem by applying shape generalization and a combination of simple shape features that are analysed over time. Exercise types are recognised by means of binary silhouette sequences extracted from the [PERSON] database [21] with added occlusions and two-stage classification. Firstly, coarse classification based on centroid trajectory divides actions into two subgroups--actions performed in place and actions with changing location of a silhouette.