## 2 Related Works  
An action can be defined as a single person short activity composed of multiple gestures organised in time that lasts up to several seconds or minutes [1,16], e.g., running, walking or bending. Many actions can be performed for a longer time than several minutes, however due to their periodic characteristic only a short action span is used for recognition. The recognition process is here understood as assigning action labels to sequences of images [17]. Then, action classification can be based on various features, such as colour, grey levels, texture, shape or characteristic points like centroid or contour. Selected features are numerically represented using specific description algorithms in a form of so-called representation or descriptor. According to [2], good representation for action recognition has to be easy to calculate, provide a description for as many classes as possible and reflect similarities between look-alike actions. There is a large body of literature on video-based action recognition and related topics investigating wide variety of methods and algorithms using diverse features. An interest is reflected in the still emerging surveys and reviews (e.g., [2; 18; 19; 20; 21]). Due to many techniques on action classification reported in the literature, here we refer to several works that correspond to our interests in terms of the methods and data used. The authors of [23] propose a novel pose descriptor based on the human silhouette, called Histogram of Oriented Rectangles. The human silhouette is represented by a set of oriented rectangular patches, and the distribution of these patches is represented as oriented histograms. Histograms are classified by different techniques, such as nearest neighbour classification, Support Vector Machine (SVM) and Dynamic Time Warping (DTW), among which the last one turned out to be the most accurate. Another silhouette based feature was proposed in [24] which uses Trace transform for a set of silhouettes representing single period of action. The authors introduce two feature extraction methods: History Trace Templates (a sequence representation with spatio-temporal information) and History Triple Features (a set of invariant features calculated for every frame). The classification is performed using Radial Basis Function Kernel SVM and Linear Discriminant Analysis is applied for dimensionality reduction. Action recognition based on silhouettes is presented in [25] as well. All silhouettes in a sequence are represented as time series (using a rotation invariant distance function) and each of them is transformed into so-called Symbolic Aggregate approXimation (SAX). An action is then represented by a set of SAX vectors. The model is trained using the random forest method and various classification methods are tested. The authors of [26] propose a novel feature for action recognition based on silhouette contours only. A contour is divided into radial bins of the same angle using centroid coordinates and a summary value is obtained for each bin. A summary value (variance, max value or range) depends on Euclidean distances from centroid to contour points in every radial bin. The proposed feature is used together with a bag of key poses approach and tested in single- and multi-view scenarios using DTW and leave-one-out procedure. The authors of [8; 9] use accumulated silhouettes (all binary masks of an action sequence are compressed into one image) instead of every silhouette separately. In [8] various contour- and region-based features are combined, such as Cartesian Coordinate Features and Histogram of Oriented Gradients (HOG). SVM and K-nearest neighbour (KNN) classifiers are used (the latter one in two scenarios). In total, seven different features and three classifiers are experimentally tested. The highest accuracy is reported for a combination of HOG and KNN in leave-one-sequence-out scenario. In [9] an average energy silhouette image is calculated for each sequence.