# Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors
## 2 Related Works on Action Recognition and the Problem of Object Occlusion
### Selected Related Works  
One of the first works on spatio-temporal action recognition is reported in [22]. The authors propose Motion History Image (MHI) and Motion Energy Image (MEI) templates that represent how an object is moving and where the motion is present, respectively. In [23], the variants of MEI and MHI are combined to propose a new representation called temporal key poses. Instead of using one representation for a whole video sequence, a temporal template is assigned to each video frame. Then, k-nearest neighbour (KNN) and majority voting are applied for action classification. MHI is also used in [24] to create binary patterns. A texture operator, Local Binary Pattern (LBP), extracts the direction of motion from MHI and then the Histogram of Oriented Gradients (HOG) is used to represent features from LBP. Classification is performed with a Support Vector Machine (SVM).  
Another solution that uses aggregated silhouettes from the entire sequence is described in [25]. Each sequence is represented using Average Silhouette Energy Images, where higher intensity pixels refer to static motion and low intensity values represent changing movements. Based on these, Edge Distribution of Gradients, Directional Pixels and R-transform produce feature vectors that are combined in a final action representation. The authors of [26] investigate several contour and shape based descriptors indicating superiority of silhouette-based approaches. Again, action features are extracted from accumulated silhouette images, called ASI. It is indicated that almost perfect accuracy can be obtained using HOG and KNN with Euclidean distance.  
In [27], binary silhouettes are represented separately. Shape contours are converted into time series and then encoded into short symbolic representation called SAX-Shape. A set of SAX vectors represent an action, and classification is performed using a random forest algorithm. A popular concept for data reduction is the selection of key poses [28; 29]. The authors of [29] use silhouette contours and divide them into equal radial bins using centroid location. Then, a summary value of each bin is calculated based on the Euclidean distances from centroid to every contour point. The proposed feature is used in a learning algorithm to build a bag of key poses, and sequences of key poses are classified using Dynamic Time Warping and leave-one-out procedure. The idea of radial scheme is applied in [30] as well; however, the entire shape area is used (contour with its interior). Firstly, human silhouette is divided into smaller regions in a radial fashion. Then, each radial bin is represented using region-based shape descriptors--geometrical and Hu moments. Obtained feature vectors are fed into multi-class SVM that indicates action classes. Radial coordinates and centroid location are also related to polar transform, which is proposed for shape description in [31]. Three polar coordinate systems are employed to represent the whole human body, and upper and lower parts of the body. For each of these systems, a histogram is generated based on radial bins. The normalized histograms are concatenated and represent a human posture. Classification is performed on a predefined number of frames.