# Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders
## 2 Related Works  
In general, video summarization techniques are distinguished into the following main categories [2]: (a) representative frame-based selection, (b) key-frame subshot detection and (c) key object detection algorithms. The representative frame-based selection focuses on identifying a series of discontinuous frames to comprise a synopsis that represents the whole video content as much as possible. In this context, References [18; 19] propose different key frame extraction methods based on visual descriptors. On the other hand, Reference [3] performs the key frame extraction using only the temporal variations of a video sequence.  
In the context of key-subshot detection, Reference [4; 20] can be considered. These approaches extract short-time segments of a video as meaningful representations of its visual content. In Reference [21], an unsupervised video summarization algorithm is introduced that uses title-based image search results to find out shots of visual similarity. In Reference [22], the authors introduce a video summarization technique that decomposes the whole sequence into key objects. This representative selection problem is formulated as a sparse dictionary selection problem. Finally, Reference [23] identifies key-frames as a set of local interest points description and repeatability graph clustering. The selection of key frames is performed using graph clustering by approaching modularity principle.  
In choreographic context, video summarization techniques use motion variations of spatio-temporal data in order to define the most representative key frames of the dance sequence. An example of this category is Reference [6] that applies the SMRS algorithm under a hierarchical modification for video dance summary. This method captures the variations or movements of each human action in different subspaces, which allow them to be represented as sequences of transitions from one subspace to another. This work is valid only for a single dancer while its performance when multiple dancers are present severely deteriorates. In Reference [24], the problem of learning motion primitives as one of temporal clustering is addressed deriving an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA).  
HACA defines a partition of a given multidimensional time series into disjoint segments such that each segment belongs to one of clusters. HACA combines kernel \(k\)-means. In Reference [25], a robust method for detection and tracking human poses in videos is presented by matching video trajectories to a 3D motion capture model. The main novelty of this work resides in computing the correspondences between video and motion capture data. Reference [26] detects local minima in the temporal variation of the motion speed. The analysis is obtained by applying a low-pass filter to a one-dimensional motion speed data stream. In Reference [17], the authors have incorporated unsupervised clustering method for extracting key frames from choreographies. Toward this direction, classification of motion primitives of a dance using Long-Short Term Memory (LSTM) structures has been proposed in Reference [27].  
In Reference [7], motion motifs and motion signatures are represented as a succinct but descriptive representation of motion sequences. Firstly, the motion sequences decomposed to short-term movements called motion words, and then the words are clustered in a high-dimensional feature space to find motion patterns. To this end, a deep learning architecture is exploited to embed the motion words into features. In Reference [28], an exploratory search system for large data collections of motion capture data is presented. The system provides an overview of human poses in a hierarchical dendrogram visualization that represents the result of a clustering procedure. A node-link diagram enables the user to analyze human poses as nodes, where each node shows a collection of similar pose instances.