# Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition
## 1 Introduction  
Video Activity Recognition aims to automatically analyze and interpret particular events within video sequences. In the last years action recognition has gained interest, thanks to the growth of multimedia files availability and due to the amount of tasks it is useful for.  
Currently, multimedia information generates large volumes of data, and this increases the need of developing automatic or semi-automatic systems which allow the labelling of video actions with different applications. For example, video cameras record data from different environments in real-time, which is useful, for instance, in security matters.  
Many different domains can take advantage from video activity recognition, such as video security, video retrieval, or human-computer interaction. There are many different situations where a system that warns about suspicious actions in real time is highly beneficial, and since the enormous growth that multimedia data has experienced in recent years makes manual tagging tedious and sometimes impractical, video recognition can be used to perform annotation and indexing of videos. Furthermore, it can be useful to generate interactive systems for social purposes or for entertainment industry.  
Identifying human actions in videos is a complex task. It is more challenging than image recognition, where a single frame represents the whole scene. In video recognition, a frame of a video where someone appears walking could also be a frame of a sequence of someone running, more frames are needed to see what action is taking place. The complexity of this task comes from the high intra-class variability that exists between the instances. This intra-class variability is caused by different factors, such as the diversity among people both in their appearance and in the style of execution of the action,the movements of the camera, the environment, which is usually affected by changes in lighting, shadows or occlusions, the viewpoint and the distance of the subject from the camera, and other factors, such as differences in resolution. Human actions are associated with a spatial and a temporal component, both random, so the performance of the same action is never identical.  
In this paper, taking as a basis the work of [PERSON] [1], a new approach for video action recognition is presented, where Common Spatial Pattern (CSP) algorithm is used, a method which is normally used in Brain Computer Interface (BCI) for electroencephalography (EEG) systems [2]. CSP is a dimensionality reduction technique which consists of finding an optimum spatial filter to separate a multidimensional signal into two classes, maximizing the variance of one of them while minimizing the variance of the other. In our approach input videos are represented as frame sequences and the temporal sequence of each pixel is treated as a signal (channel) to feed the CSP. After CSP is applied, some signals descriptors are selected for classification purposes. In classical CSP applications, only the signal variances and Linear Discriminant Analysis (LDA) classifier [3] are used; in this research, variances, minimum, maximum, and interquartile range (IQR) are taken as descriptors, and LDA, K Nearest Neighbors (KNN) [4] and Random Forests (RF) [5] as classifiers.  
The rest of the paper is organized as follows--First, in Section 2 some related works are mentioned in order to introduce the topic. In Section 3 a theoretical framework is presented to explain the proposed approach in detail. In Section 4 the experimental setup is presented, the used data-set and the different experimentation carried out are explained thoroughly. To conclude, in Section 5 the obtained results are shown and a comparison between our approach and another method is made.