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They also use a time series of Histogram of Oriented Optical Flow (HOOF) to characterize human motion. They use Histograms of Oriented Gradients (HOG) for human pose representations, first introduced in and successfully applied in multiple action recognition methods.

The authors of present a method to recognize human actions observing them from a far field of view, but they also test their model with normal resolution datasets, such as Weizmann. Throughout this section, only motion based approaches are analyzed. Key-frame based approaches, as the name indicates, consist of detecting the key-frames of the video which would be used for classification BoW based approaches represent the frames of the video segments over a vocabulary of visual features interest points based approaches focus on simply selecting a specific set of points or pixels for the classification and, to finish, motion based approaches focus on the movement along the video. Key-frame, bag-of-words (BoW), interest points and motion based approaches are types of representations that can be obtained from a video. Temporal visual features are a combination of static image features and time information, so, through these features, temporal video information is achieved. There are several ways to extract visual features, both static image features and temporal visual features, and then use them to perform the recognition. This document focuses on video-based activity recognition, in which the representation of visual and temporal information becomes important. There are other variations such as egocentric activity recognition that consists of recognizing actions from egocentric videos. In the cross-view action recognition, there are different points of view in the scene when the action is occurring.

To address this issue, complementary information of invisible classes is assumed in the form of attribute vectors that describe each class. The zero-shot action recognition problem consists of training a model to classify videos of categories that have no instances in the training set, which means that there are no instances of certain classes that are going to appear in the test set. In action prediction, instead of recognizing the action that is happening in the video, the objective is to guess the action that will occur in an incomplete video. For the sake of completeness, we will briefly review the main characteristics of the situations shown in Figure 1 but not covered here. At the same time, the complexity level of the problem considered in this review is high enough to deserve a dedicated survey. Only action recognition from a whole video recorded from a fixed position is considered in this paper, as we think this problem setup is the entrance gate to the analysis of other more complex situations, as those presented in the bottom part of Figure 1. This review focuses on a specific area of Human Action Recognition, to keep the discussion simple. Based on this, the integration of commonsense reasoning and contextual knowledge has been proposed. This aptitude is not just related to acquired knowledge, but also to logical reasoning and the capability of extracting relevant information from context. On the contrary, the human brain seems to have the ability to recognize human actions perfectly. If object recognition techniques are needed (a challenging problem in its own), further complexity is added. The aim of video tracking is to associate target objects in consecutive video frames, which can be especially difficult if the objects are moving fast in relation to the frame rate.

Video tracking is more challenging than the previous approach and can be very time consuming, due to the amount of data that a video contains. One of the simplest ways to detect motion regarding a fixed background is Video Motion Detection. In the analysis of a video content, many different functionalities can be implemented.